Roxane Bertrand

Aurélie Goujon

(Dés)aligner pour améliorer la compréhension mutuelle en interaction (Dis)aligning for improving mutual understanding in talk-in-interaction

Keywords: mutual understanding, (dis)alignment, dispreferred response, interactional activity Résumé compréhension mutuelle, (dés)alignement, réponse non préférée, activité interactionnelle

. This allows us to shed light on the different levels of understanding required for the accomplishment of successful interaction. Feedback responses provided by recipients are one of the explicit marks for achieving understanding. However, we focus here on dispreferred responses that can be considered a cue of disalignment, i.e. a break in the activity in progress and a potential indicator of trouble in understanding. Using the Conversational Analysis framework, we show how and why recipients can disalign and to what extent the mutual understanding is impacted by this disalignment.

Introduction

We discuss here the "alignment/disalignment" phenomenon required for achieving successful communication in talk-in-interaction. To succeed, participants need to understand each other. From a psycholinguistic point of view, alignment facilitates comprehension: In the Interactive Alignment account [START_REF] Pickering | Toward a mechanistic psychology of dialogue[END_REF]), when participants align, they tend to imitate each other. Garrod and Pickering (2009, 298) argued that "comprehension involves imitating what has been heard using the production system, and then using those representations to make predictions". From a linguistic point of view, for Conversation Analysis researchers, understanding is not considered a "cognitive, private, individual phenomenon" but a collaborative one involving "situated, contingent, embodied, and intersubjective dimensions" (Mondada 2011, 542). Mutual understanding is then considered a collaborative achievement that the concept of alignment enables us to address in a relevant way : On the one hand, participants tend to align their mental representations by imitating each other at different linguistic and non linguistic levels of representation (referential, phonological, postural among others) [START_REF] Garrod | Joint Action, Interactive Alignment, and Dialog[END_REF]; On the other hand, alignment also relates to cooperation at a structural level "by facilitating the proposed activity or sequence; accepting the presuppositions and terms of the proposed action or activity" (…) (Stivers et al. 2011, 21). In the present paper, we will take into account this under-studied area of structural level of alignment for a better understanding of mutual understanding in talk-in-interaction. As already said, an interaction would be successful when participants achieve alignment. Structurally, more specifically, an aligning response in storytelling conversation for example could be a 'mh' or 'yes' whereby the recipient allows the main speaker (storyteller) to achieve several turns for telling [START_REF] Stivers | Stance, alignment, and affiliation during storytelling: when nodding is a token of affiliation[END_REF][START_REF] Guardiola | Interactional convergence in conversational storytelling: when reported speech is a cue of alignment and/or affiliation[END_REF][START_REF] Bertrand | Co-narration in French conversation storytelling: A quantitative insight[END_REF]. Providing another type of response -an unexpected one -, could be considered a cue of disalignment, i.e. a break in the activity in progress and a potential indicator of trouble in understanding.

Our question is how and why recipients disalign and to what extent the mutual understanding is impacted by this disalignment? Using the sequential approach of the Conversation Analysis (Now CA) framework, we examine several sequences displaying disalignment cues -both verbal and non verbal -in different interactional tasks or activities.

Overview

How people coordinate is one of the fundamental issues of language use [START_REF] Clark | Using Language[END_REF]). An example of such coordination is the turn-taking system [START_REF] Sacks | A simplest systematics for the organization of turn-taking for conversation[END_REF]) that illustrates how understanding each other and understanding what is going on in the interaction enable participants to predict and provide a potential turn alternation within the appropriate timing.

Achieving coordination requires aligning that improves (mutual) understanding. This issue raises a great interest in different fields. Psycholinguistics for example focuses on cognitive mechanisms involved in the understanding process (see Pickering & Garrod 2004, among others). These cognitive aspects are out of scope here. Conversation Analysis studies are more often concerned not with understanding in itself but rather "in the basic methods through which understanding is established and maintained" (Koschmann 2011, 436). More specifically, Mondada claims: "understanding is not treated as a mental process but is related to the next action achieved by the co-participant and demonstrating her understanding" (2011,543). This illustrates the way in which this notion has often been treated. Indeed, it fits with the objective and method of CA researchers that consist in examining how participants, turn-by-turn, incrementally, construct interaction in a collaborative way that of course requires understanding yet without focusing on it.

Similarly, we are questioning how participants, thanks to explicit marks of alignment/disalignment, communicate to each other recognizable mutual understanding related to different levels of participants' coordination.

Before giving a brief overview of the Conversation Analysis literature examining how explicitly participants achieve understanding, it is noteworthy that it is not always easy to investigate it insofar as it is very often achieved tacitly (Mondada 2011, 544). This can partly explain why once we consider the issue of understanding, issues of misunderstanding are immediately brought up, both being different faces of the same phenomenon, but also maybe because the more explicit marks of misunderstanding make the latter a better candidate for analysis. However, except tacit understanding cases, Sacks (1992) distinguished two ways for explicitly showing understanding: Recipient can "claim" understanding by using a simple repeat of the prior turn or he can "demonstrate" it by using reformulation. Here again [START_REF] Mondada | Understanding as an embodied, situated and sequential achievement in interaction[END_REF] notes that this demonstration can also provide evidence of a misunderstanding since it can initiate a repair 1 .

More generally, achieving a mutual understanding requires to provide appropriate responses that allow the interaction to go on. Different alternative responses are possible among them a preferred/dispreferred one. This issue of preference has been largely investigated at a sequential level, particularly in adjacency pair sequences: for example in response to a question or an offer, an answer and an acceptation respectively are the preferred responses. Understanding also means predicting and anticipating, hence the importance of expected responses as preferred ones. Feedback items are one of the most studied expected responses of mutual understanding. Feedback items, also called "verbal listener response" [START_REF] Dittman | Relationship between vocalizations and head nods as listener responses[END_REF], "backchannel response" [START_REF] Yngve | On getting a word in edgewise[END_REF], "acknowledge acts" [START_REF] Sinclair | Towards an analysis of discourse: The English used by Teachers and Pupils[END_REF], "minimal feedback" [START_REF] Holmes | Story-telling in New Zealand women's and men's talk[END_REF], "minimal responses" [START_REF] Fishman | Interactions: The work women do[END_REF], "receipt tokens" [START_REF] Atkinson | Displaying neutrality: formal aspects of informal court proceedings[END_REF]) (see [START_REF] Gardner | When Listeners Talk[END_REF] for a more exhaustive list) have been investigated in different research fields [START_REF] Allwood | The MUMIN coding scheme for the annotation of feedback, turn management and sequencing phenomena[END_REF][START_REF] Bunt | Context and dialogue control[END_REF] in semantics; Gravano & Hirschberg 2011 in prosody;Ward & Tsukahara 2000 in Natural Language Processing;Clark & Fox Tree 2002 in psycholinguistics). Whatever the framework, it is generally admitted that this type of behavior "appears to provide the auditor with a means for participating actively in the conversation, thus facilitating the general coordination (we highlight) of action by both participants" (Duncan & Fiske 1977, 202-203).

The substantial literature on feedback responses has been directed towards their form (mh, yeah, oh, repetition, nod, eyebrow movement) and their functions (continuer, acknowledgement, change-of-state token, confirmation request) as well as their sequential 1 Repair is defined by Schegloff (1977) as "the set of pratices whereby a co-interactant interrupts the ongoing course of action to attend to possible trouble in speaking, hearing or understanding the talk". environment. In the 1980s, after establishing the now well-known continuer versus assessment dichotomy, Schegloff (1982, 87-88) said that continuers (mostly including Mm, Mh hm, Uh huh or yeah) "display recipient's understanding that an extended turn at talk is in progress but not yet complete, while simultaneously collaborating in the achievement of that multi-unit utterance by passing the opportunity to either (a) produce a more extended turn of their own or (b) initiate repair on the talk just heard". On the other hand, assessments refer to brief tokens such as "wow", great", "wonderful" and so on. They are designed to provide a form of appreciation that "Mm" for example cannot provide.

Following Goodwin:

Assessments can occur in roughly the same environment as 'uh huh,' for example in the midst of extended turns at talk by another. However they are not treated like 'uh huh' by speaker, and indeed appear to have a different sequential organization. Briefly, rather than bridging two turn-constructional units (as with continuers, added by us) assessments in the midst of another's extended talk come to completion before a new unit is entered (1986,209).

By investigating a larger project such as storytelling, [START_REF] Stivers | Stance, alignment, and affiliation during storytelling: when nodding is a token of affiliation[END_REF] allows us to expand the notion of preferred response to a broader span dealing with a level of alignment mainly concerned with the structural level of activity or action. For example, storytelling is an asymmetrical activity involving a main speaker (storyteller) who needs several turns to achieve his/her project and a recipient. Several studies have shown that feedback responses or continuers such as "mh", "yeah", "nod" for the most frequent, were preferentially produced at the beginning of stories by recipient [START_REF] Bavelas | Listeners as co-narrators[END_REF][START_REF] Stivers | Stance, alignment, and affiliation during storytelling: when nodding is a token of affiliation[END_REF][START_REF] Guardiola | Interactional convergence in conversational storytelling: when reported speech is a cue of alignment and/or affiliation[END_REF][START_REF] Bertrand | Co-narration in French conversation storytelling: A quantitative insight[END_REF]. In doing so, the interlocutor aligns with the current activity: Firstly, by accepting to become recipient of the story; Secondly, by providing such minimal responses as the story progresses, he/she explicitly gives cues of the elaboration of the common ground. Thus, these minimal responses provide evidences of the recipient's understanding while revealing a form of 'preference for progressivity' [START_REF] Stivers | A preference for progressivity in interaction[END_REF]. Sacks (1992) shed light on "understanding positions", i.e. specific sequential environments within talk for "doing understanding". For the author, the completion of a story is an example of such a position, "where recipients can exhibit their possible understanding, for example by using a proverbial expression" (1992, II, 426). Similarly, by using the concept of "affiliation" dealing with the expression of stance (mood, emotions, and so on) [START_REF] Stivers | Stance, alignment, and affiliation during storytelling: when nodding is a token of affiliation[END_REF] has shown that assessments, unlike continuers, would appear at the end of the story preferentially, when recipients have sufficient information for understanding and expressing a more evaluative response.

An interaction would thus be successful when participants align and understand each other.

Consequently, a dispreferred response could be considered a cue of disalignment, i.e. a potential indicator of trouble in understanding. Let us go back to the point concerning the 'understanding position'. This deals with the localization and timing of responses. Indeed, it is noteworthy that a response occurring too early or too late in the activity in progress could reveal a disalignment and a problem in understanding [START_REF] Stivers | Stance, alignment, and affiliation during storytelling: when nodding is a token of affiliation[END_REF][START_REF] Selting | The display and management of affectivity in climaxes of amusing stories[END_REF]). In such cases, the lack of ratification by the main speaker provides evidence of a potential understanding problem [START_REF] Bertrand | Listing practice in French conversation: From collaborative achievement to interactional convergence[END_REF]. This paper is designed to investigate feedback responses as potential triggers of disalignment. After examining where and why these responses can appear, we will see that disalignment, more specifically at structural level, can be a mean for repairing or preventing a problem in understanding but also a mean for exhibiting another level of understanding dealing with the intersubjectivity level that does not really lead to a misunderstanding.

Aims, Data and Methodology

Talk-in-interaction implies that people are engaged in different types of activities such as explaining, storytelling, making others laugh, and so on. Following Lindström and Sorjonen (2014, 353), "any given response is shaped by at least the following: (i) the sequential place in which affiliation -(we replace by alignment in the present work) -is to be displayed, (ii) the type of action to which the response should be given, and (iii) the larger activity in progress".

Based on several extracts involving explanation and storytelling, we will show what typical feedback responses say about mutual understanding, when they structurally and punctually disalign (in terms of sequential environment) within the larger activity in which they are embedded.

Our following analysis is based on two French audio-video corpora recorded in the 'Parole et Langage' laboratory (LPL).

The French Map-Task corpus

The Maptask corpus [START_REF] Gorisch | Aix Map Task corpus: The French multimodal corpus of task-oriented dialogue[END_REF]) includes video recordings for both participants individually so as they could see each other during the dialogue. The Maptask corpus is a collaborative task in which speakers are directors or followers, round after round, and they have to track back a pathway on a paper. 4 pairs of participants performed seven map tasks and each map took about 5 minutes to complete. Participants were recruited at the LPL.

They were researchers, post-doctoral researchers and master's degree students and knew each other. The corpus was recorded in an anechoic room. Each speaker was equipped with a microphone headset enabling the recording of both speakers' voices on different sound tracks in order to allow a fine-grained analysis of overlap phases, namely with regard to phonetics and prosody. The Maptask corpus exhibits a main activity of explanation and presents factual information concerning the direction and the location of elements on the map.

The Corpus of Interactional Data

The CID [START_REF] Bertrand | Le CID -Corpus of Interactional Data-Annotation et Exploitation multimodale de Parole Conversationnelle[END_REF]) is an audio video recording of French face-to-face conversations (8 pairs of speakers, 8 hours, about 115,000 words). Like for the previous corpus, the CID was recorded in an anechoic room and each speaker was also equipped with a microphone headset. The participants were asked to talk about either unusual situations (3 dyads) or contentious professional situations (5 dyads) in which they were involved.

Despite the task and the setting, the protocol was thought up to favor conversational interaction. On the one hand, not only all the participants were colleagues in the same University, but the members of each dyad were also friends used to meeting up outside work. On the other hand, bringing friends together for one entire hour left room for many digressions.

The advantage of using these two types of data for examining different manifestations of mutual understanding is linked to their different intrinsic nature (in terms of goal particularly), and their embedding in a different larger task.

Method

Using Praat [START_REF] Boersma | Praat: Doing Phonetics by Computer (Version 5.1.05[END_REF] the speech signal of both corpora was presegmented into Inter-Pausal Units (IPU) defined as speech blocks punctuated by silent pauses of at least 200ms. IPUs are not linguistic units although some studies can be conceived them as turns [START_REF] Koiso | An analysis of turn-taking and backchannels based on prosodic and syntactic features in Japanese Map Task dialogs[END_REF]). In our work, they are formal objective units facilitating the subsequent manual orthographic transcription. From this transcription, both corpora were annotated at different linguistic levels (temporally aligned) [START_REF] Blache | Multimodal annotation of conversational data[END_REF][START_REF] Prévot | A CUP of CoFee: A large Collection of feedback Utterances Provided with communicative function annotations[END_REF]. The different phenomena investigated were systematically annotated on the whole corpus. After that, and as it is the case in this present paper, we propose a notation a posteriori of phenomena within the given transcription of extracts (see conventions in the end of the article).

In this paper, the main phenomenon concerns feedback items that were annotated for both corpora (for details see CoFee Project: http://cofee.hypotheses.org/). However, other different levels of annotation were used here. For the CID, we used the annotation level of stories (for details see [START_REF] Bertrand | Co-narration in French conversation storytelling: A quantitative insight[END_REF]. For the Maptask, we used the annotation level of eyebrow movements. Until now, only the Maptask was entirely annotated at this level. Examples considered here are extracted from Goujon (thesis, in preparation) who questions how participants can make a source or problems identifiable by focusing on eyebrow movements. On the one hand, this work aims at filling the gap concerning multimodal feedback responses (see Mondada 2011, 545). On the other hand, by focusing on eyebrow movements as a potential trigger of disalignment, this work also contributes fully to the scope of this paper, i.e.to enhance the knowledge of (mis)understanding and (dis)alignment relationship. The pre-segmentation and annotation of eyebrow movements were realized with the segmentation mode of ELAN [START_REF] Sloetjes | Annotation by category -ELAN and ISO DCR[END_REF].

Eyebrow raises and frowns were manually annotated. In order to do so, it is necessary to watch the video at a slowed rate to find the exact image corresponding to the eyebrow movement. If it is an eyebrow-raising movement, the eyebrows gradually rise up and go back to a neutral position on a vertical axis. If it is a frowning movement, the eyebrows move horizontal axis. Eyebrows move toward each other near the center and a line appears between them. It is important to note that the annotated movements can be on a single eyebrow.

Given the objective of the thesis work, the annotation of sequences revealing a potential trouble in understanding was also performed. Among relevant cues for identifying such sequences, discourse markers such as "attends", "j'ai pas compris", "excuse moi" ("wait", "I didn't understand", "excuse me") and the presence of a repair sequence, both showing a break in the progressivity of discourse, were used. 84 sequences were extracted, among them 29 were produced with eyebrow movements. Three categories of sequences revealing a potential problem in understanding have been identified: non-comprehension and incomprehension cases [START_REF] Weigand | Misunderstanding: The standard case[END_REF], and a check for understanding case [START_REF] Antaki | Affiliative and disaffiliative candidate understandings[END_REF].

Our following analysis will introduce a case of each category established in the Maptask.

We will show what kind of specific disalignment cues participants use for indicating and/or solving a potential trouble in understanding within each category, first in the Maptask corpus, secondly in the CID.

Our approach is quite original since we adopted a hybrid method based on Corpus Linguistic approach and Conversation Analysis. Within a sequential analysis, the next-turn proof procedure [START_REF] Hutchby | Conversation Analysis: Principles, practices, and applications[END_REF] enables us to shed light how each turn is subsequently interpreted by the other participant, in regard to the prior and the next turn and how turn after turn (incrementally), participants demonstrate their mutual (mis)understanding (to each other).

Analysis

Referential (dis)alignment and non comprehension

The first level of understanding requires a referential alignment (cf. "situation model", [START_REF] Pickering | Toward a mechanistic psychology of dialogue[END_REF]. A disalignment can be due to a lack of common referents.

The nature of a Maptask corpus is particularly suitable for investigating explanation activity at referential level since available references in each of maps are not similar between both participants. Indeed, the purpose of this type of task is to show how participants, step by step, construct their knowledge and, in most cases, successfully achieve the task. While the director gives instruction, the follower has to follow it despite their different map. The necessary collaborative achievement of this task involves a quasi turn-by-turn tracking.

Ambiguity is something that such an Instruction task corpus can in no way afford (understanding needs to be checked at almost every turn). Thus although understanding may be tacit [START_REF] Mondada | Understanding as an embodied, situated and sequential achievement in interaction[END_REF], it is mostly explicitly performed in such a corpus by using feedback responses as continuers/acknowledgements and agreements (mh, yeah, nod, repetitions). In this task, an aligning response would be a yes, ok, or an absence of response followed by the accomplishment of the instruction.

The example (1) illustrates a non-comprehension case [START_REF] Weigand | Misunderstanding: The standard case[END_REF] due to a lack of knowledge by the recipient. Transcript conventions can be found at the end of this article.

Example (1) (CS directeur)

The trouble is immediately identified because the director gives a referent that does not exist on the follower's map. Thus, the follower immediately displays disalignment cues related to the lack of the same referent ('pirogue') in his own map. Disalignment cues combine both an eyebrow raising movement (conveying a function of surprise or doubt) and an explicit verbal utterance expressing the source of the trouble. We can see an immediate resolution from the director who, after taking this missing referent into account with the 'oui' (line 3) item, immediately suggests another referent. This latter is immediately ratified by the follower with a combined response composed of 'ouais' + repetition + eyebrow raising movement allowing participants to realign from this new element to go on with the task. Both eyebrow raising movements seem to function as formal boundaries of the reparation sequence.

Example (2) is extracted from the CID. We want to show here how participants can also try to align at a referential level in a conversation-style corpus in which implicit and ambiguous elements can be less dramatic than in a Maptask corpus. We show that when the interlocutor attempts to align at the referential level, she simultaneously conveys cues related to the intersubjective dimension revealing a larger collaborative activity that her partner may or may not take into account in the understanding process.

The extract (2) appears at the beginning of one interaction involving two females. In order to recount and share stories as agreed (i.e. tell unusual situations), participants attempt at aligning regarding the meaning of the term used, i.e. 'insolite'. However, among the 8 dyads of the corpus, only this one shows such an attempt of explicit initial alignment at a referential level before starting the dialog. simultaneously shows an orientation to AB with whom she will now share referents to achieve a first level understanding (talking about the same things with the same term) that will ensure the success of their dialog. By opening this activity of explanation, CM disaligns with the previous action by which AB seemed to position herself as the next storyteller. However, neither of them really had speakership (vs the director/follower in the maptask) and CM (line 6) makes no hesitation in interrupting AB's projected action by displaying her state of affairs about the instruction (more particularly about the term 'insolite') in an overlap with AB's turn. Sequentially, her utterance projects an answerresponse even if she does not provide a direct question. We can see (line 11) that AB gives the expected (preferred) response, i.e. showing that she has understood the indirect demand:

The adjacency pair (line 7-line 11) then respects the preference and conditional relevance principles ("an answer is conditionally relevant after a question", [START_REF] Schegloff | Opening up closings[END_REF] and perfectly fits with the sequential notion of understanding [START_REF] Schegloff | Repair after next turn: the last structurally provided for place for the defence of intersubjectivity in conversation[END_REF]. By providing complex feedback (line 14), composed of a change-of-state token 'ah oui' + other-repetition 'inhabituel' + an agreement 'ouais' overlapping with part of AB's turn, CM very clearly expresses her alignment with AB that reveals a perfect understanding.

However, and although both participants seem henceforth aligned at a referential level and

Disalignment and incomprehension case

The example (3) illustrates an incomprehension case [START_REF] Weigand | Misunderstanding: The standard case[END_REF]) due to a misinterpretation of an utterance: the message sent by the speaker is not received properly. MB tells a story concerning her work and the bad habit (according to her) her colleagues have of scheduling meetings at lunchtime. She explains that such a schedule is totally counterproductive and she initiates a list in which each item is proof of this counter productivity. The rude manner in which MB, the main speaker, shows she wants to continue speaking (line 13) by ignoring the list item from AG (line 11-12), can be explained by the fact that MB is engaged in the orientation phase of her story (at the beginning). The goal of the two lists she produced just previously is not to illustrate something she had already said, which could have allowed the listener to co-elaborate the discourse, at very least to show her agreement, but to present the characters and circumstances of events in the story and then prepare the ground for establishing her own opinion and justify it. This list item from AG occurred too early for MB who considered it a disaligned response. Her lack of ratification here provides special access to (mis)understanding. This is totally in line with Stivers about nodding (2008) or Selting about laughter (2017) for whom a response occurring too early (or too late) reveals a disalignment and a problem in understanding. The following example (5) illustrates another case of the relationship between (dis)alignment and understanding. This is a case of storytelling in which the recipient punctually disaligns at the beginning of the story.

Example (3) (YM directeur)

Example (4)

The extract is related to two males. Following the instructions (to tell stories of professional conflict), it is the first story of the dialog. When he was in Belgium, EB worked with a colleague on developing computer softwares. The conflict was not about the content but about the form. His colleague wondered whether they had to use upper or lower case letters, which was absolutely ridiculous for EB. show a lack of understanding from the recipient. Far from introducing repair, on the contrary he reacts on the content word in order to detract it from its focus while claiming his intention to make fun of the current story. This reflects what is being performed in the whole dialog task even if SR will respect the instruction throughout the interaction.

Example (5)

EB, as main speaker, does not only agree to engage in this new frame but overbids (line 7-8) on SR's proposal resulting in a side humorous sequence [START_REF] Jefferson | Side sequences[END_REF].

Then, after an initial disalignment from recipient, we can also observe a fully aligned sequence (depending on whether the main speaker aligns or not) showing a perfect mutual understanding achievement.

In our opinion, this disalignment could mean that the leadership of the teller can sometimes be delayed, more particularly in conversation. This delay can be considered a resource for indicating that the recipient is not a passive listener in orienting the interaction in a specific way.

Disaligning for checking understanding

Check for Understanding cases [START_REF] Antaki | Affiliative and disaffiliative candidate understandings[END_REF] are produced in order to anticipate potential misunderstandings. These are polar questions which appear in a morally problematic situation [START_REF] Stivers | The morality of knowledge in conversation[END_REF]. The questions have an interrogative structure but their content is partially asserted.

Example (6) (AM directeur)

1 AM mh d'accord donc t'es en dessous ensuite tu les 2 contournes sur la droite donc tu remontes 3 CS <je les contourne sur la droite> 4 AM euh non pardon sur la gauche @ excuse moi 5 c'est moi @ tu les contournes sur la gauche 6 tu remontes 7 CS @@ah d'accord ok ok@@ ouais ouais 1 AM mm ok so you are below then you will by pass 2 them on your right so you will to go back 3 CS I'll by pass on the right <> 4 AM hum no pardon on the left @ excuse me it's 5 me @ you will by pass 6 them on the left you will go back 7 CS @@ ah right okay okay @ yeah yeah Among the responses, other than simple continuer provided by the follower, otherrepetitions (as confirmation request), can be used to Check For Understanding (CFU). The example (6) shows such a CFU (associated with eyebrow movements at its beginning and its end) allowing the director to realize his error. A self-initiated repair is then performed to avoid failure of comprehension. Feedback response from CS shows not only that he has correctly understood the repaired turn but also that he had anticipated a potential trouble before expressing his CFU given that the function of this feedback (change-of-state => 'ah d'accord' line 7, that we could paraphrase as 'I thought so! That's strange').

Example (7) involving the same pair of female speakers of ( 2) again illustrates how one participant uses disalignment as a resource for signalizing another form of understanding related to the speakers' relationship in real life. The disalignment from CM can be explained by several reasons: First, it is not the kind of story requested by experimenters; Secondly, we know that CM is one of the best listeners of the CID providing a lot of feedback when necessary. By choosing to disalign, CM gives evidence that the relationship between herself and AB in real life is more important than the structural format required when a story is projected.

Example (7)

Elements of conclusion

Using the notion of alignment/disalignment proposed by [START_REF] Stivers | Stance, alignment, and affiliation during storytelling: when nodding is a token of affiliation[END_REF], we claim that mutual understanding covers a larger acceptation than what is traditionally admitted. In such a conversationalist perspective indeed, mutual understanding is not conceived as a cognitive individual process but rather as a collective and collaborative activity [START_REF] Mondada | Understanding as an embodied, situated and sequential achievement in interaction[END_REF]. Mutual understanding does not only refer to the meaning of the words or sentences exchanged but to the meaning/comprehension of actions and activities in which participants are involved as well as the different positions and stance they adopt towards each other.

Feedback responses play a crucial role in the achievement of mutual understanding.

However, as unexpected responses, they can be used as triggers of disalignment and can reveal a potential trouble or problem of understanding. The examination of different sequences of storytelling and explanation embedded in larger tasks (conversation or instruction task) enables us to better understand why and how some typical feedback responses can disalign for some times initiating a repair but also for conveying other levels of understanding dealing with intersubjectivity.

In the Maptask corpus, we highlighted typical cues of disalignment, e.g. eyebrow movements and change-of-state token ("ah") both expressing a form of surprise, that more or less lead to a repair sequence. In this case, such disaligned responses require stopping or postponing the accomplishment of the instruction. By disaligning, the follower shows this problem leading the director to repair it. The repair sequence can then be an explicit way to (re)align. Given the nature of such a corpus, it is crucial to understand each other for achieving the task in a successful way. Participants can then disalign for expressing 'noncomprehension' [START_REF] Weigand | Misunderstanding: The standard case[END_REF] dealing with a referential disalignment, or for expressing 'incomprehension' [START_REF] Weigand | Misunderstanding: The standard case[END_REF] dealing with a misinterpretation of discourse.

'Checking for understanding' [START_REF] Antaki | Affiliative and disaffiliative candidate understandings[END_REF], produced for anticipating a potential trouble in understanding, can also cause a disalignment potentially followed by a repair.

The examination of the conversational corpus (CID) shows that if similarly disalignment cues aim at preventing a potential misunderstanding namely due to a referential lack, participants can use referential alignment as a resource for conveying a level of understanding related to the interaction itself and to the relationship between both participants. Furthermore, recipients can also use a disaligned response as a resource for orienting the interaction in a particular frame (for example humor) showing that he/she has well understood what is going on but more importantly that he/she is a real actor and not a passive listener for achieving current activity/interaction in a successful way. Finally, recipients can also provide an inappropriate response because occurring too early. In this case, the lack of ratification of this response makes it a disaligned one and points out misunderstanding related to the current activity [START_REF] Stivers | Stance, alignment, and affiliation during storytelling: when nodding is a token of affiliation[END_REF][START_REF] Selting | The display and management of affectivity in climaxes of amusing stories[END_REF][START_REF] Bertrand | Listing practice in French conversation: From collaborative achievement to interactional convergence[END_REF].

To conclude, this notion of (dis)alignment really seems to be a relevant conceptual tool to improve our knowledge on how participants mutually show each other (mis)understanding.

Future research including more specifically facial movements as potential cues or triggers of (dis)alignment could be conducted in a more systematic way thanks to large databases now available. More globally, further research could improve knowledge about more global disalignment cues in order to implement them into virtual conversational agent studies.

Transcript conventions

Elision: the characters related to the omitted phonemes are written between parentheses:

"petit" pronounced /pti/ is noted p(e)tit.

Truncated words: annotated with a final dash.

le li-le livre (the book).

Missing liaisons : # trois # amis (missing required liaison)

Onomatopoeia: the typical back-channel onomatopoeia /m/ produced by the hearer is transcribed as mh when it was realized with one syllable, and mhm for two syllables.

Incomprehensible sequences annotated with a star: * Laughters: @ Said while laughing: @@ …@@ Pauses: long pauses (more than 200ms) are automatically detected and enable to identify

  Here, once again the follower disaligns by providing a dispreferred response. But in this case, although sufficient information was available to understand what the director said, the follower misunderstood the location of the zone. The director cannot avoid the misunderstanding sequence that persists for several turns because the repair is not immediate. After several acknowledgement tokens, from line 7, AG produces a confirmation request initiated by a change-of-state token (ah) and associated with a raised eyebrow aiming at checking the understanding. These different cues conveying a form of surprise appear as the main trigger of disalignment. After that, the director's response (line 8) reveals the misunderstanding that will be followed by a long sequence of repair: As the trouble source of repair, the utterance line 9 (with another confirmation request, still initiated by a change-of-state token 'ah') marks the beginning of the incomprehension sequence. Then the director gives a repair solution (line 11), after which the follower shows comprehension of the trouble source. The ratification of repair solution ("d'accord ok") is given in line 18. This example is an illustration of the explicit achievement from misunderstanding to understanding (see also the reiteration of the same change-of-state token 'ah' conveying surprise or doubt at each step of the repair sequence), after a first step of disalignment. The last step of realignment occurs when the follower ratifies (line 18) the repair of the director resulting in mutual understanding.The Example (4) illustrates a typical incomprehension case in conversation storytelling.In a recent paper about listing practice in storytelling[START_REF] Bertrand | Listing practice in French conversation: From collaborative achievement to interactional convergence[END_REF], we have shown that a list item added by a recipient within a list produced by a storyteller, can be considered the best candidate for showing a mutual understanding. However, this list item is considered an appropriate/aligned response if it occurs at the right place at the right time. The example (4) (extracted from[START_REF] Bertrand | Listing practice in French conversation: From collaborative achievement to interactional convergence[END_REF] shows how the recipient of the list embedded in a story failed to understand that she had not yet the legitimacy for providing his/her response.

  IPU. The shorter perceptible pauses are notated with "+" and very long pauses are sometimes indicated in the transcript (0.700 ms) Overlaps: the words or utterances in overlapped were underlined bah alors est-ce que je vais remonter c'est ça n'évoque rien euh […] refer to the entity on the maptask Eyebrow movement < > fast eyebrow movement < … > eyebrow movement occurring on the whole segment

  in telling "unusual stories", they cannot do it: indeed, AB does not yield the floor without completing the definition. In doing so, she does not focus (she goes on to ignore it) on the feedback response provided by CM. Contrariwise, along with the three very long pauses(13, 17, 21) in the middle of her turn, AB illustrates to what extent she

	1 2 3 4 5 6 7 8 ready to involve remains focused on her own discourse in 1/ projecting a more to come as if she was AB well then CM * well curious look for AB curious AB its a good subject AB well then should I go back up CM does it not make you think of anything CM uh I'm not sure I know i really know the definition
	9 10 completely legitimate as main speaker and could pause without risking to loose the turn and CM of uh CM curious
	11 12 2/ also ignoring the clear demonstration of understanding from CM. The sequence (lines AB mi just think of it as something like that unusual infrequent
	13 15-24) then illustrates a kind of disaligned sequence in which both participants try to give and something quite (0.736)
	14-> CM 15-> CM their own definition in a most exhaustive way. Globally, it is noteworthy that while CM ah yes unusual, infrequent yeah i imagine yes particular things
	16 17 exhibited a more collaborative attitude by not only requesting a definition from AB, but AB also + particular (0.674) CM where uh
	18 also by aligning with this definition, and finally by eliciting a cue of alignment from AB AB that aren't don't jump you in the eye
	19 20 ('tu vois' line 22), AB does not even demonstrate such a cue. When she provides the like CM yes where you lose a bit uh you lose
	21 22 minimal token 'mh' at the end of her explanation, AB seems to be merely ratifying her own a bit how can I say(0.981) CM well points of reference like you see
	23 prior explanation unlike CM (line 26) who rather seems to ratify their joint activity of its because
	24	AB	that can be extraordinary
	25	AB	mm
	26-> CM	its uh yeah well yeah	(1.260)
	27	CM	well @ nothing nothing curious has
	28		ever happened to me absolutely nothing
	29		@	(1.547)
	30	CM	uh
	31	AB	everything has been normal and logical (0.358)
	32	CM	yes absolutely @
	33	CM	curious

definition. This initial explanation sequence, embedded in a larger activity of narrative conversation, illustrates the need for partners in conversation to share referents (referential alignment) for mutual understanding. However, it also reveals a participant (CM) very oriented to her partner (see Bertrand & Espesser 2017 who showed that CM was one of the 'best' listeners) versus a less collaborative one (AB). We may then wonder if AB has really completely understood what CM did with her turn, e.g. showing her desire to collaborate with her partner without hesitating to convey a lack of order to collaboratively change the current state of affairs. For her part, AB demonstrates her understanding of the only referential lack: yet understanding is of course far from being a simple alignment at a referential level.

  EB starts his story by displaying deictic elements usually used in the orientation phase to introduce a story. Immediately, while such a deictic element in storytelling was not intended for discussion, it is here used by SR for disaligning (line 5). Previous studies[START_REF] Bertrand | Does prosody play a specific role in conversational humor?[END_REF][START_REF] Guardiola | Interactional convergence in conversational storytelling: when reported speech is a cue of alignment and/or affiliation[END_REF] have shown that typical responses such as other-repetitions or echo reported speech can be relevant cues for introducing a new tonality or a new frame. However this more frequently happens in the middle or at the end of the story when both participants have all information allowing them to understand and evaluate it. By initiating this unexpected response here, SR does not

	1	EB	ouais ça c'était ap ob assez
	2		particulier c'était euh c'était quand
	3		je bossais en Belgique, euh
	4	EB	et euh
	5	SR	ah oui c'est vrai tu as bossé en Belgique
	6		je euh retire tout ce que j'ai dit euh @
	7	EB	@
	8	EB	@ j'ai une expérience internationale
	9	SR	@ euh international
	10	EB	@
	11	SR	@ même en Belgique, ils nous l'achètent @
	12	EB	une fois
	13	EB	@
	14	EB	et euh
	15	EB	et c'était euh c'est pourquoi on s'était pris
	16		la tête
	17	EB	ah oui parce qu'elle jouait sur les mh
	18		ah oui y av c'était une histoire de de mh
	19	EB	de mh
	20	EB	on faisait on f on faisait on était on
	21		faisait un un logiciel
	22	EB	et euh dans le logiciel on avait des
	23		euh des des commandes utilisateurs
	24	EB	et mh et y avait eu on avait mis des
	25		majuscules à tous les noms
	1	EB	yes that was aft a bit strange
	2		particulier it was uh it was when i
	3		was working in Belgium, T/ uh
	4	EB	and uh
	5	SR	ah yes thats it you worked in Belgium
	6		I uh take back everything I said uh @
	7	EB	@
	8	EB	@ i had an international experience
	9	SR	@ uh international
	10	EB	@
	11	SR	@ even in Belgium, T/ they get it @
	12	EB	once
	13	EB	@
	14	EB	and uh
	15	EB	and it was uh that's why we fell out
	16	EB	ah yes because she was playing on the
	17		mh ah yes there w it was a such
	18		a crazy story
	19	EB	crazy mh
	20	EB	we were we were working on some
	21		software
	22	EB	and uh in the software we had some uh
	23		some user commands some
	24	EB	and mm and where were we had put
	25		capital letters on all the names

  This extract just follows a discussion about a movie that CM has seen and has just described to AB. The latter seems to have appreciated it since she starts by saying 'j'irai le voir je l'ai noté' (line 1). Just after this, AB starts to relate why she might not be able to go to see it. She projects a story and immediately CM aligns as recipient by providing three continuers before understanding that AB had been around that weekend. By creating a failure in the progressivity of the activity projected by AB ('je l'ai appelée'), CM (line 9) disaligns. This disalignment however, is neither a means of initiating repair nor of threatening AB but rather a means for CM to show to what extent she has understood what AB just said and the potential future implications about their relationship: AB had been there this weekend and CM did not call her to invite her to join them. By disaligning to justify herself, CM displays a demonstration[START_REF] Sacks | A simplest systematics for the organization of turn-taking for conversation[END_REF]) of her understanding. Once again, although AB's turn respects the conditional relevance implying an answer-response here, she fulfills it minimally with a simple 'oui' before going on with her story. CM however does not give up and provides another component aiming at evaluating this answer and justifying herself. AB's turn confirms her answer (aligns) but minimally ("yes she was here" but she does not provide more for showing that she does not hold it against CM). The sequence ends with the minimal acknowledgment provided by CM who now lets AB going

	1	AB	j'irai le voir je l'ai noté
	2	CM	ouais ouais ouais ouais non va le voir c'est
	3	AB	en fait je devais y aller avec ma
	4		copine Sabine
	5		parce qu'elle était là ce week-end son
	6		copain partait à Paris quand je l'ai appelée
	7	CM	oui
	8	CM	mh
	9	CM	ouais ah mais tu étais là ce week-end
	10	AB	oui elle avait une gastro
	11	CM	ah bah c'est con
	12	CM	si on avait su on te euh je pensais que tu étais
	13		pas là moi sinon euh on t'aurait proposé
	14	AB	si en fait je suis restée là et euh
	15	CM	ouais
	16	CM	ah oui
	17	AB	et euh
	18	AB	elle avait une gastro donc elle m'a dit §*
	19	CM	ah ouais
	20	AB	je suis malade depuis hier soir et §
	21	CM	oui
	1	AB	I'll go and see it i wont forget
	2	CM	yeah yeah yeah yeah no go and see it its
	3	AB	actually i was gojng to go with my friend sabine
	5		because she was around this weekend her
	6		boyfriend was going to paris when i called her
	7	CM	yes
	8	CM	mm
	9	CM	yeah ah but you were around this weekend
	10	AB	i was sick
	11	CM	ah well thats a shame
	12	CM	if id known i thought you weren't
	13		around or we would have asked you
	14	AB	yeah in fact i stayed here and uh
	15	CM	yeah
	16	CM	ah yes
	17	AB	and uh
	18	AB	she was sick so she told me §*
	19	CM	ah yeah
	20	AB	I ve been sick since yesterday night and
	21	CM	yes

on with her story. By repeating the prior phrase 'elle avait une gastro' AB shows her realignment with her initial story. At this point, CM accepts and (once again) becomes recipient enabling the progressivity of the story (lines 19 and 21).

Acknowledgments

The authors wish to thank Béatrice Priego-Valverde and two anonymous reviewers for their comments and feedback on the present paper.


Sébastien Angelliaume
email: sebastien.angelliaume@onera.fr

Xavier Ceamanos
email: xavier.ceamanos@onera.fr

Françoise Viallefont-Robinet

Rémi Baqué
email: remi.baque@onera.fr

Philippe Déliot
email: philippe.deliot@onera.fr

Véronique Miegebielle
email: veronique.miegebielle@total.com

Hyperspectral and Radar Airborne Imagery over Controlled Release of Oil at Sea

Keywords: SAR imagery, hyperspectral imagery, radar sensor, optical sensor, multi-frequency, multi-wavelength, oil slick, oil spill, seeps, maritime pollution

Remote sensing techniques are commonly used by Oil and Gas (O&G) companies to monitor hydrocarbon on the ocean surface. The interest lies not only in exploration but also in the monitoring of the maritime environment. Occurrence of natural seeps on the sea surface is a key indicator of the presence of mature source rock in the subsurface. These natural seeps, as well as the oil slicks, are commonly detected using radar sensors but the addition of optical imagery can deliver extra information such as thickness and composition of the detected oil, which is critical for both exploration purposes and efficient cleanup operations. Today state-of-the-art approaches combine multiple data collected by optical and radar sensors embedded on-board different airborne and spaceborne platforms, to ensure wide spatial coverage and high frequency revisit time.

Multi-wavelength imaging system may create a breakthrough in remote sensing applications, but it requires adapted processing techniques that need to be developed. To explore performances offered by multi-wavelength radar and optical sensors for oil slick monitoring, remote sensing data have been collected by SETHI, the airborne system developed by ONERA, during an oil spill cleanup exercise carried out in 2015. The uniqueness of this data set lies in its high spatial resolution, low noise level and quasi-simultaneous acquisitions of different part of the EM spectrum. Specific processing techniques have been developed in order to extract meaningful information associated with oil-covered sea surface. Analysis of this unique and rich dataset demonstrates that remote sensing imagery, collected in both optical and microwave domains, allows to estimate slick surface properties such as the age of the emulsion released at sea, the spatial abundance of oil and the relative concentration of hydrocarbons remaining on the sea surface.

Introduction

In the context of offshore hydrocarbon exploration and production, Oil and Gas (O&G) companies thrive to discover new oil fields. It is also their duty to exploit them in a clean and safe manner. Field exploration demands more and more efficient and high performance techniques to discover new reservoirs in increasingly extreme environments. This exploration area has a high probability of being located under a large body of water that does not allow direct access to the bottom of the ocean and requires the use of long range interpretation techniques. Early detection of the occurrence of natural seeps, on the ocean surface, is a key indicator of the presence of mature source rock in the subsurface. Furthermore, remote sensing technology [START_REF] Fingas | Review of oil spill remote sensing[END_REF] [START_REF] Brekke | Oil spill detection by satellite remote sensing[END_REF] is also essential in the global monitoring of offshore production sites. The ability to respond rapidly, based on accurate information, to threatening incidents enables O&G companies to minimize the impact and to reduce risk of offshore operations.

Today, optical and radar sensors are currently available either from space or onboard aircraft.

Combining multi-wavelength imagery appears like a breakthrough in remote sensing applications [START_REF] Leifer | State of the art satellite and airborne marine oil spill remote sensing: Application to the BP Deepwater Horizon oil spill[END_REF] [START_REF] Jha | Advances in Remote Sensing for Oil Spill Disaster Management: State-of-the-Art Sensors Technology for Oil Spill Surveillance[END_REF], but it requires specific processing techniques. Methodologies for detecting hydrocarbons at sea are typically based on radar imagery [START_REF] Gade | Using ERS-2 SAR images for routine observation of marine pollution in European coastal waters[END_REF] [START_REF] Girard-Ardhuin | Operational oil-slick characterization by SAR imagery and synergistic data[END_REF], mainly as a result of weather-related issues that require data on which cloud cover has little impact.

Oceanic and atmospheric phenomena (called look-alike) can occur over the ocean surface and manifest themselves on radar images in the same way (dark patches) as area covered by hydrocarbons. Distinction between natural oil seeps, oil spill (accidental event) and radar look-alikes, such as phytoplankton bloom or low wind areas, may be tricky when looking only at radar data, but could be done by combining radar and optical remote sensing data. Optical and radar imagery collected over the same area and almost in the same time, allows scientists to progress in this issue. In addition, the complementarities of multi-frequency radar data [START_REF] Angelliaume | Multifrequency Radar Imagery and Characterization of Hazardous and Noxious Substances at Sea[END_REF] and multi-wavelength optical data [START_REF] Lennon | Combined passive hyperspectral imagery and active fluorescence laser spectroscopy for airborne quantitative mapping of oil slicks at sea[END_REF] are also an important way of improvement in the context of oil-slicks monitoring.

To assess and solve these issues, TOTAL (the French petroleum company) and ONERA (the French Aerospace Lab) have for several years embarked upon the development of innovative remote sensing techniques to detect and monitor ocean surface covered by hydrocarbons, having in mind both the exploration and the environmental monitoring of off-shore operations. The activities, which are part of the NAOMI (New Advanced Observation Method Integration) project, investigate performances in the field of detection, characterization and quantification of slick-covered sea surface provided by radar and optical imagery.

In order to sustain these activities, remote sensing data have been collected by SETHI, the airborne system developed by ONERA, over controlled release of mineral oil at sea during the oil-on-water (OOW) exercise carried out by NOFO (Norwegian Clean Seas Association for Operating Companies) in 2015 off the Norwegian coast. This experiment at sea is primarily dedicated to testing mechanical recovery systems. Several sensors were operated onboard SETHI and have imaged the spill areas: dual-frequency Synthetic Aperture Radar (SAR) imaging in a full polarimetric mode and optronic sensors composed of one panchromatic camera (aligned with the radar viewing angle) and two hyperspectral cameras (nadir imagery) covering the full range of 0.4-2.5 µm with a very high spectral resolution. This paper presents the airborne sensors operated by ONERA during the oil spill cleanup exercise organized by NOFO in 2015 and demonstrates the added-value brought by multi-wavelength remote sensing imagery collected in both optical and microwave domains to carry out oil-slick monitoring. It is organized as follows: Section 2 describes the experiment at sea managed by NOFO, Section 3 presents in detail the remote sensing sensors operated onboard SETHI during the airborne campaign of measurements, Section 4 gives the results obtained in both optical and microwave domains and main discussions are in Section 5.

Experimentation at Sea

During the oil spill cleanup exercise organized by NOFO in 2015, mineral oil was released at sea in a control manner, with the main objective of testing mechanical recovering system in a realistic marine environment. The oil-on-water exercise was carried out in the Frigg field (located in the North Sea between the Norway and the United Kingdom, 230 km northwest of Stavanger) within 10 Nautical Miles of position (59° 59'N, 02° 27'E), from the 8 th to the 14 th of June.

In this paper, we focus on remote sensing data acquired on June 9, during the so-called MOS Sweeper exercise (Figure 1). MOS Sweeper [START_REF] Mos | [END_REF] is a multi-barrier system in V-shaped pattern operated from a single vessel, which guides the oil towards the rear and center of the system where an integrated pump transports the oil directly to the towing vessel. For the experiment, hydrocarbons are released from a floating pump at the sea surface and towed by another vessel (Stril Mariner). The distance between the two vessels is only a few hundred meters. MOS Sweeper trial began on 9 th June in the early morning (06:00 am UTC) and continued until 10:30 am UTC. After a first phase of low flow discharge (06:00 -06:30 am UTC), the release continued between 06:30 am and 08:00 am at full regime. The boats were advancing towards the west-north-west (heading 290°) at a speed of 2.5 knots. Around 08:00 am, the lead vessel (Stril Mariner) had completed the release and Stril Luna, the boat towing the MOS Sweeper system, turned around and continued the cleaning process of the slick until 10:30 am. At the time of data acquisition, it was heading towards the east-south-east (heading 100°) at a speed of 2.5 knots, following the drift of the oil slick.

The released product is an emulsion of mineral oil in water, which was produced by NOFO and consists of a mix of water, Oseberg crude oil and a small addition of IFO 380 (Intermediate Fuel Oil or marine diesel oil, with viscosity of 380 mm 2 .s -1 ). The water content of the recovered emulsion was measured by NOFO and is equal to 60%. For the trial, 45 m 3 of mineral oil emulsion was discharged at sea and recovered by the MOS Sweeper system.

During the experimentation, sea state information was provided by the Norwegian Meteorological Institute and is given in Table 1 below. This information is consistent with that measured from one of the vessels present during the offshore experiment and given in [START_REF] Skrunes | A Multisensor Comparison of Experimental Oil Spills in Polarimetric SAR for High Wind Conditions[END_REF].

Airborne Campaign of Acquisition

SETHI is the ONERA airborne remote sensing laboratory [START_REF] Baque | SETHI and RAMSES-NG -Flexible multi-spectral airborne remote sensing research platforms[END_REF] designed to explore the science applications of remote sensing. It is onboard a Dassault Aviation Falcon 20 that can fly up-to 30 000 ft above sea level, with 2.5 hours of autonomy.

It combines two pods under wings which are able to carry heavy and cumbersome payloads The radar antennas can be oriented and moved during operation. This flexibility is provided by motorized antenna that can be horn, patches array, dipoles and parabolic antennas. The incidence angle of each antenna can be changed during the flight with a high accuracy (0.1 degree). This is a significant capability enabling to explore the incidence angle effect on clutter or target signature.

This motorization is also used to point different radars on a same area for simultaneous multi-frequency imagery. Figure 3 illustrates possible geometries of imagery for both optical and radar sensors.

Hyperspectral Sensors

The optronic sensors, installed in the SETHI pods, are composed of one panchromatic camera (CamV2 [START_REF] Souchon | The IGN CAMv2 System[END_REF], developed by the French National Mapping Agency, IGN -Institut Geographique National) and two HySpex VNIR1600 and SWIR320m-e hyperspectral cameras built by NEO (Norsk Elektro Optikk) [START_REF]HySpex Hyperspectral Cameras -Norsk Elektro Optikk[END_REF]. The main parameters of optical sensors operated during the NOFO oil-on-water exercise are given Table 2 below. These HySpex push-broom cameras imaging synchronously offer an effective complementarity in the spectral domain: VNIR1600 covers the full range of 410 to 996 nm with 160 spectral bands with a spectral sampling of 3.6 nm while the SWIR320m-e based on a Mercury Cadmium Telluride -Focal Plane Array (MCT FPA) covers the extended domain of 970 to 2500 nm with a spectral sampling of 6 nm. They offer a total of 410 different spectral bands providing a high spectral resolution of the scene. The spectral overlap between the VNIR and SWIR cameras allows the quick assessment of the proper calibration of the instruments. The along track and across track instantaneous field of view (iFOV) are equal to 0.37 x 0.185 mrad for the VNIR camera and 0.75 x 0.75 mrad for the SWIR camera (pixels of the SWIR camera are twice larger along track and four times larger across track than the VNIR ones). With 1600 pixels for the VNIR camera and 320 pixels for the SWIR one (across track) the resulting field of view is about 17° and 14° respectively for the VNIR and SWIR sensor. To reduce time lag between radar and optical acquisitions, as explained in the next paragraph and illustrated by Figure 8, the same altitude, namely 9000 ft (Flight Level 90), driven by radar needs, is used for the two spectral domains.

To ensure a flight speed compatible with the cameras frame rates, at this altitude, the plane must fly with a ground speed around 120 m.s -1 . This speed is quite slow for the Falcon 20, which is a jet plane, which induces a pitch angle of about 5°. This angle had to be mechanically offset to ensure a nadir line of sight. The assembly was done in France and the travel to Norway was at high altitude which implies quite low temperatures inside the pods. Moreover, to cope with temperature issue inside the pod during measurements in Norway, the optical sensors were covered by an insulated material and were maintained in a correct range of temperature with a flat heating resistance triggered by a thermostatic sensor. This heating device is also used to avoid ice deposits on the glass of the porthole.

Another issue that had to be considered concerned the vibrations inside the pod during acquisition.

A specific study was carried out to measure the vibration spectrum and to eliminate its impact. First, low-frequency shock absorbers installed in the pod achieved a partial removing. The residual movement is generally at such a low frequency when it occurs that it is finally compensated by post-processing using the data from the IMU.

Dedicated software has been developed by ONERA for processing and archiving image products such as georeferenced spectral radiance and spectral ground reflectance products. Processing consisted in applying calibration coefficients on the hyperspectral images to obtain radiance products in raw geometry (level 1a), which take into account the transmission through the glass of the pod. From level 1a, image is corrected for atmospheric effects (level 2a) using the ONERA atmospheric tool COCHISE [START_REF] Miesch | Direct and inverse radiative transfer solutions for visible and near-infrared hyperspectral imagery[END_REF], which is based on the use of hyperspectral information combined with the radiative transfer code MODTRAN [START_REF] Berk | MODTRAN4 radiative transfer modeling for atmospheric correction[END_REF]. As scan lines are synchronized with altitude and position measured by IMU it is possible to get the position of each pixel in the image geometry map.

These geo-referenced images are projected onto an UTM map grid to achieve levels 1b and 2b, i.e.

ortho-rectified images in units of spectral radiance or reflectance. As the time schedule was driven by the trial of the mechanical recovering boom during the exercise at sea, it was not possible to fit the images acquisitions to the best meteorological conditions. During the NOFO campaign, most of the images were collected with the presence of sparse-cloud cover. In the following, we focus on areas that were not or slightly affected by clouds. In the latter case the spectral reflectance obtained after atmospheric correction would be only slightly degraded.

Microwave Sensors

SEHTI radar sensors operated during the OOW'2015 experiment where the L-and X-band ones.

The waveform used for this airborne campaign of measurements is presented Table 3 below. To achieve best sensitivity and reduce the radar noise figures, LNA (Low Noise Amplifiers) have been placed inside the pod, close to the receiving antennas. To limit transmit losses, the X-band HPA (High Power Amplifier) has also been integrated inside the pod close to the transmitting antenna.

Thanks to very low losses cables between cabin and pods (at low frequencies), the L-band HPA is installed inside the cabin with the AWG, the sampling/record systems and back-end microwave modules (see Figure 5). For the OOW'2015 experiment, L-and X-band antennas are oriented at a fixed 45° of incidence angle. To reach an illuminated area of minimum 1.5 km in X-band, the minimum flying altitude is equal to 9000 ft. L-band antenna elevation aperture is bigger than X-band one so common illuminated area is limited by the X-band geometry (see Figure 6). Radar PRF (Pulse Repetition Frequency) has been selected regarding Range and Doppler ambiguities: 10 kHz is chosen so useful PRF for each SAR polarization image is 5 kHz (each polarization -H and V -is transmitted every two radar recurrences). To maximize the radar signal to noise ratio (SNR) without reducing too much the full resolution swath (that corresponds to sampled swath duration minus pulse duration), the chosen transmitted pulse duration is 20 µs (transmit duty cycle 20%).

To record the complete illuminated area, the sampling system is configured with a 27.5 µs sampling window duration for each recurrence and each received channel. Regarding the incidence angle and the transmitted pulse duration, the obtained full resolution swath is equal to 1.57 km, in ground range geometry. This allows scene observation at incidence angles ranging from 34° to 52° across the swath. The radar acquisition trajectory is a 10 km long straight lines (90" record duration), so SAR images cover 1.57 x 10 km² areas with spatial resolution (in slant range) of 0.5 x 0.5 m² in X-band and 1.0 x 1.0 m² in L-band.

Waveform parameters have been selected to reach the best compromise between large swath (improved with high altitude) and high signal to noise ratio (improved with small range). The Noise Equivalent Sigma Zero (NESZ) has been estimated using the method proposed in [START_REF] Hajnsek | Inversion of surface parameters from polarimetric SAR[END_REF]. It takes very low values (see Figure 7), ranging from around -37 to -50 dB at X-band and from around -51 to -53 dB at L-band, allowing a sufficiently high SNR for efficient analysis over sea surface covered by oil.

Campaign of Acquisition

Optical and radar sensors used for this campaign have been presented above (see paragraphs 3.1. and 3.2.); we focus now on the data acquisition process. SETHI was flying at an altitude of 9 000 ft., along a trajectory describing a racetrack, of which longest length measures approximately 10 km (see Figure 8).

Hyperspectral and Radar Imagery over oil slick

Imagery in Optical Domain

Discrimination between oil slicks and other phenomena (look-alike) was shown to be feasible with the use of optical imaging technique [START_REF] Andreoli | Hyperspectral Analysis of Oil and Oil-Impacted Soils for Remote Sensing Purposes[END_REF]. Furthermore, distinction between oil types and indications on oil volume can be achieved thanks to hyperspectral imaging [START_REF] Clark | A Method for Quantitative Mapping of Thick Oil Spills Using Imaging Spectroscopy[END_REF].

Airborne data collected by SETHI on the 9 th of June at 10:12 am UTC (run 10) were selected to study the potential of hyperspectral imaging. The VNIR and SWIR images in reflectance units were registered to get access for every pixel to the full spectral signature from 0.4 to 2.5 microns. This was done using the GeFolki toolbox developed by ONERA [START_REF] Brigot | Adaptation and Evaluation of an Optical Flow Method Applied to Coregistration of Forest Remote Sensing Images[END_REF] based on the common spectral range in the two images. A RGB (Red-Green-Blue) quick-look of the flight line is shown in Figure 9 -(a). At the time of the acquisition, 10:12 am UTC, there were some sparse clouds under and above the aircraft. Nevertheless, the oil slick was clearly visible in overall. The red box in Figure 9 -(a) shows the location of the region of interest (ROI) that was selected for the upcoming data analysis.

A RGB color composite of the selected ROI is shown Figure 9 -(b). This ROI was chosen for the high abundance of oil emulsion. Moreover, the presence of clouds in the image provides a robustness test of the analysis tools. Several elements are represented in the ROI: the oil slick, seawater and clouds.

Within the oil slick we can distinguish three regions: a bright red region at the center of the slick, some light red pixels surrounding the first region and a whitish halo that is commonly called the sheen. The spectra acquired by the hyperspectral cameras over these five elements of the image (the three identified above within the slick as well as the clouds and seawater) are shown in Figure 9 -(c) in units of reflectance. As it can be seen, the signature of seawater (flat except for a slightly higher reflectance before 0.6 micrometers) is predominant for all elements except for the bright red region.

This area contains the highest abundances of oil emulsion. There, oil emulsion shows a positive reflectance slope starting at 0.6 micrometers and three reflective peaks at 1.3, 1.65 and 2.2 micrometers. Furthermore, the characteristic absorption peaks of hydrocarbons are seen at 1732 and 2310 nanometers [START_REF] Cloutis | Spectral Reflectance Properties of Hydrocarbons: Remote-Sensing Implications[END_REF]. These spectral attributes are only visible where the emulsion abundance is high. In the case of the light red region of the oil slick, however, the emulsion abundance is less important according to the strongly faded spectral attributes of oil emulsion due to a higher presence of the seawater signature. The spectral reflectance of the sheen is very close to the signature of seawater except for a constant offset starting at 0.6 micrometers and a milder negative spectral slope between 0.4 and 0.6 micrometers. Clouds present a similar spectral signature than seawater but with a higher reflectance value. The FI index provides information on the contrast between the blue and the red wavelengths, which is mainly shaped by the reflectance decrease of seawater before 0.6 micrometers. The presence of oil may reduce the spectral slope in this spectral domain. Figure 10 -(left) shows the FI score map obtained from the investigated hyperspectral data. The oil slick is clearly detected, as well as its different regions (from dark red color at the center of slick to green color for the sheen). However, the high reflectance of clouds generates false alarms with FI values close to those of the slick.

The RAI index also exploits the spectral slope in the VNIR but goes further into the NIR domain by using the spectral band at 850 nanometers. At this wavelength, the oil emulsion reflectance may be higher than at 470 nm due to the reflectance increase of oil in the VNIR. The multiplication by the norm of the two spectral bands (2) roughly compensates the denominator and thus amplifies the difference effect (see numerator). Figure 10 -(center) shows the RAI score map for the selected hyperspectral image. The slick is well detected and the clouds are less visible. However, the use of the NIR band makes the result noisy, as the VNIR camera is less performant in terms of SNR for wavelengths close to 1 micron. Spectral indices are also available in the SWIR range for monitoring oil slicks. Among them, we selected the Hydrocarbon Index (HI), which measures the depth of the typical absorption band of hydrocarbons at 1.73 micrometers [START_REF] Kühn | Hydrocarbon Index -an algorithm for hyperspectral detection of hydrocarbons[END_REF]. Although other indices are based upon the absorption peak at 2.3 micrometers, they are usually less performant due to the weaker absorption compared to the 1.7 microns one and the lower signal reaching the detector in this spectral range (the solar spectral illumination decreases according Planck's law). The HI index is calculated as follows The results provided by the nFi index can be exploited to generate quick look products to be used for oil monitoring. Figure 12 -( In addition to detection, hyperspectral imaging is also able to characterize oil slicks by estimating their properties such as emulsion type, thickness and water content [START_REF] Clark | A Method for Quantitative Mapping of Thick Oil Spills Using Imaging Spectroscopy[END_REF]. This can be achieved if the spectral properties of the oil emulsion are known in advance. Blind characterization of oil slicks (with no a priori information) is very challenging due to the variability of the spectral signature according to many parameters. One of these parameters is the age of the emulsion or the elapsed time between the generation of the emulsion and imaging of the released product.

( ) ( ) ( ) B A A C A C A B HI ρ ρ ρ ρ λ λ λ λ - + - - - = (3) where A ρ , B
A sample of the oil emulsion was collected on the day of the campaign with the aim of characterizing the oil slick. The sample was sent by NOFO to ONERA a month later and the spectral reflectance of the emulsion, which was already in a disaggregated state, was measured later with a spectroradiometer. Afterwards, the emulsion was reconstructed on December 5th by mixing it until a homogeneous dense mixture was obtained. Spectral reflectance was regularly measured from that day on, and during several months, to study the temporal evolution of the emulsion signature.

Figure 13 -(left) shows the spectral library that was built using some of the measurements. This spectral library is used in the following experiment to estimate the age of the emulsion observed by the HySpex cameras, onboard SETHI during the NOFO experiment. This is done by carrying out an image classification following a spectral matching approach. The spectral distance Spectral Information Divergence (SID) is used to compute a similarity score between a given spectrum of the investigated hyperspectral image and each class spectrum of the library [START_REF] Chang | An information-theoretic approach to spectral variability, similarity, and discrimination for hyperspectral image analysis[END_REF]. Hence, each image spectrum is classified as the class providing the lowest SID score (i.e., the highest similarity). The SID approach is widely used in processing of hyperspectral images due to its high sensitivity to the shape of spectra.

Given two spectra, x and y with N values (N being the number of spectral bands), the SID score is calculated as follows ( )

∑ ∑ = = + = N i i i i N i i i i x y y y x x y x SID 1 ' ' ' 1 ' ' ' log log , (5)
Where

∑ ∑ = = = = N i i i i N i i i i y y y x x x 1 ' 1 ' , (6)
For this experiment, a classification threshold was used to classify only the spectra with a SID score inferior or equal to 0.05. In this way, only the regions of the image that are similar to at least one class of the library are classified. As it can be seen in Figure 13 -(right), the classification map only reveals the center of the slick, which contains the purest pixels of the oil emulsion. The predominating class is the one corresponding to December 12 th , thus estimating the emulsion age to be around one week. This corresponds approximately to the time lag between the preparation of the emulsion by NOFO and its release into the sea the day of the airborne campaign.

Figure 13.

(left) Spectral library of the spectral reflectance between 0.4 and 2.5 micrometers for the NOFO oil emulsion over time. The "disaggregated" spectrum corresponds to the measurement that was done before reconstructing the emulsion with the mixer -(right) Classification map obtained with the SID approach and the spectral library. The color of pixels follows the color code of the spectral plots.

The last experiment aims at quantifying the areas of the image in which seawater coexists with the oil emulsion in the same pixel. Figure 14 -(a) shows a picture taken from the boat showing this coexistence. This phenomenon leads to observe some spectra measured by the HySpex cameras (corresponding to a 1m 2 region at the sea surface in the VNIR case) resulting from linear mixture of the signatures of seawater and oil emulsion. The spatial abundance of each component can be quantified by the oil areal fraction, that is, the cover fraction of the oil emulsion in a given pixel. This parameter is quantified for the investigated hyperspectral image by creating a new spectral library.

Starting from a spectrum of clean seawater and a pure spectrum of the oil emulsion, several linear combinations of the two spectra are done with a step of 10%. Both endmember spectra result from averaging the spectra of a given region of the image. For the oil emulsion, the region corresponding to the bright red slick in Figure 12 is considered. The resulting spectral library is shown in Figure 14 -(b).

The spectral matching approach SID is applied again onto the investigated hyperspectral image using the spectral library according to oil areal fraction. The resulting classification map shown in Figure 14 -(c) confirms the center of the slick as the richest region in terms of oil emulsion with an areal fraction of 70% or higher. The areal fraction drops drastically if one moves away from the center of the slick, down to 20-30% of areal fraction for the sheen. Some pixels are not classified as they do not reach the classification threshold (see black pixels). This result may point the fact that all spectral variations within the oil slick in the test image cannot be explained by the changes in areal fraction. Previous studies have demonstrated the effectiveness of the Polarization Difference (PD) for slick-covered area detection [START_REF] Kudryavtsev | On Dual Co-Polarized SAR Measurements of the Ocean Surface[END_REF][START_REF] Hansen | Wave Breaking in Slicks: Impacts on C-Band Quad-Polarized SAR Measurements[END_REF]. PD is defined such as

0 0 HH VV PD σ σ - = (7)
where 0 PP σ is the Normalized Radar Cross Section (NRCS -in linear units) and the subscript p denotes either H or V polarization. As the non-polarized part of the backscattered response is removed using PD [START_REF] Kudryavtsev | On Dual Co-Polarized SAR Measurements of the Ocean Surface[END_REF], this parameter is proportional to the spectral density of the sea surface roughness taken at the Bragg wavelength [START_REF] Guerin | The weighted curvature approximation in scattering from sea surfaces[END_REF], kB=2kEMsinθi, where kEM = 2π/λEM is the electromagnetic wavenumber corresponding to the radar wavelength λEM and θi is the local incidence angle of the EM wave. Hence, PD is mainly driven by the contribution due to short wind waves around the Bragg wavenumber, namely the capillary and gravity-capillary waves. This is precisely this scale of waves which is damped by oil when covering the ocean surface, which makes PD an efficient quantity for slick detection. To facilitate its use, a normalization of PD has been proposed in [START_REF] Angelliaume | Multifrequency Radar Imagery and Characterization of Hazardous and Noxious Substances at Sea[END_REF] 1 0 1 ≤ ≤ -= NPD PD PD NPD seawater [START_REF] Lennon | Combined passive hyperspectral imagery and active fluorescence laser spectroscopy for airborne quantitative mapping of oil slicks at sea[END_REF] where PDseawater is a value taken over the slick-free surface. NPD quantity is close to zero over clean sea water and goes to 1 as the concentration of oil locally increases.

The Normalized Polarization Difference (NPD) maps computed at X-and L-band over controlled release of mineral oil are shown Figure 16. In both cases, the oil slick is easily detectable. At X-band, the slick seems homogeneous and NPD values are always close to 1 (Figure 16 -( To overcome this ambiguity, one must refer to the Polarization Ratio, defined in linear units as

0 0 VV HH PR σ σ = (9)
The Polarization Ratio (PR) depends on the local incidence angle and the relative dielectric constant but is, to the first order, independent of the sea surface roughness [START_REF] Minchew | Determining the mixing of oil and seawater using polarimetric synthetic aperture radar[END_REF]. Thus, PR allows distinguishing between slick-covered sea surface and oceanographic phenomena [START_REF] Hansen | Wave Breaking in Slicks: Impacts on C-Band Quad-Polarized SAR Measurements[END_REF] through the difference in the dielectric constant between the clean sea surface and the polluted seawater [START_REF] Minchew | Determining the mixing of oil and seawater using polarimetric synthetic aperture radar[END_REF]. At L-band, the thickness of the oil-slick is probably too low (typically, the thickness of such layer is of the order of micrometre to millimetre [START_REF] Holt | SAR imaging of the ocean surface[END_REF]) compared to the penetration depth to be seen by the radar and to affect the Polarization Ratio (Figure 18

Summary of performances assessment of multi-wavelength imagery for oil-slick monitoring

In the optical domain, VNIR and SWIR hyperspectral cameras have been operated with a very high spectral resolution. Based on imagery collected by SETHI over mineral oil-slick, key results have been given. First, a new spectral index in the VNIR domain, called nFI (norm Fluorescence Index), is proposed. It is efficient to detect oil-covered sea surface, while being robust against clouds without suffering from low instrumental SNR. In addition to detection, estimation of oil-covered surface properties by hyperspectral sensors has been explored. The employed methodology is based on the Spectral Information Divergence (SID) which computes a measure of similarity between the spectral response (pixel-based approach) collected by SETHI and each class spectrum of a given library. The originalities proposed in this paper are: first, the operated sensors acquire the full spectral range of 410 to 2500 nm with a very high spectral resolution. Secondly, the spectrum library used has been specifically designed for this purpose. It includes spectral reflectance regularly measured in laboratory for several months, which allows the study of the temporal evolution of the oil spectral signature. Following this approach, the estimated age of the hydrocarbons imaged by hyperspectral sensors onboard SETHI is in agreement with the time lag between the preparation of the emulsion by NOFO and its release into the sea the day of the exercise. Quantifying the areas of the image in which seawater coexists with the oil emulsion in the same pixel has also been assessed.

The results show that the oil areal fraction has a strong impact on the spectral signature. As expected, the center of the slick is the part with the highest spatial abundance of oil emulsion.

However, the obtained classification map shows that all spectral variations within the oil slick cannot be explained only by the changes in areal fraction.

In the microwave domain, studies based on data acquired by the SIR-C/X-SAR imaging radar system flown aboard the space shuttle in 1994 and operating at L-(1.275 GHz), C-(5.172 GHz) and X-bands (9.677 GHz) have assessed performances of multi-frequency SAR imagery for maritime pollution monitoring. As example, it has been demonstrated in [START_REF] Gade | Imaging of biogenic and anthropogenic ocean surface films by the multifrequency/multipolarization SIR-C/X-SAR[END_REF] the capability of such data to distinguish between mineral oil and biogenic films (look-alike). As no multi-frequency instrument has been launched in space since this mission, researchers have had little opportunities to pursue multi-frequency analyses over slick-covered areas. Studies using SAR data acquired in several frequency bands over sea surface covered by oil are proposed in [START_REF] Latini | Multi-frequency and polarimetric quantitative analysis of the Gulf of Mexico oil spill event comparing different SAR systems[END_REF] and [37], but multi-frequency images are not simultaneous and are acquired by spaceborne sensors characterized by a high instrumental noise floor. X-and L-band polarimetric SAR data acquired by SETHI during the oil-on-water exercise and related analysis presented in this paper demonstrate the added value brought by multi-frequency polarimetric radar sensors to monitor sea surface covered by mineral oil. Using different EM wavelengths, the radar signal simultaneously interacts with hydrodynamic mechanisms of different scales (related to the Bragg wavelength) and then increased information collected by the remote sensing system. High frequency (e.g. X-band) is suitable for detecting oil-covered areas [START_REF] Wismann | Radar signatures of marine mineral oil spills measured by an airborne multi-frequency radar[END_REF] and also for assessing the efficiency of a mechanical recovering system operating during a decontamination phase. Lower frequency imagery (e.g. L-band) permits to evaluate the relative concentration of oil within a slick-covered area and then guide response activities towards the areas that are the most affected by the released substance. The efficiency of results obtained at X-and L-band and presented herein relies in the very low instrumental noise floor offered by SETHI, which allows valid analysis of oil-covered sea surface properties. the dataset provides a unique opportunity to explore the synergy between the two types of imagery.

One first observation is that the extent of the observed slick is much larger with radar than with optical imagery. This complementary has not yet been addressed and will be studied in a future work, both with the joint analysis of the images and with associated ocean surface modeling.

Figure 1 .

 1 Figure 1. MOS Sweeper mechanical recovering system operated by NOFO during trial at sea (photographs provided by NOFO).

( 2 .

 2 3 m long, 53 cm diameter and 120 kg of payload each pod) ranging from VHF-UHF to X-band and optical sensors with a wide range of acquisition geometries. The optronic sensors offer very high spatial resolution visible images (0.4-0.8 µm) and fine spectral scene analysis in the VNIR (0.4-1.0 µm) and SWIR (1-2.5 µm) ranges. The pod-based concept allows the easy integration and testing of new systems under the single certification of the pods by authorities. Radar payload configuration flexibility allows full polarimetric and very high resolution modes (up to centimeter), along track interferometry (ATI) and across track interferometry (XTI) and very high precision multi-baseline capacity for interferometry and tomography applications.The cabin (88 U and 480 kg) and pod capacities allow payload boarding flexibility that justifies SETHI interest as an airborne remote sensing research platform. Its flexibility is also demonstrated regarding the sensor geometry and configuration possibilities. The high dynamic and high data rate performances of the digital core are useful for large area observation (large swath) and simultaneous multi-wavelength area imaging.

Figure 2 .

 2 Figure 2. SETHI platform (aircraft, pod and cabin layout) and in-pod payload configuration examples.

Figure 3 .

 3 Figure 3. SETHI -possible geometries of imagery for both optical and radar sensors.

  The panchromatic camera is mounted inside the right pod. The hyperspectral ones are mounted inside the nose of left pod (Figure4-(a)). HySpex sensors and the IMU are fixed on the same base plate aiming approximatively in the same sight angle (Figure 4 -(b)). The offset in the sight angle is processed after acquisition using ground control points. The GPS antennas are installed on the top of each pod above the camera.(a): Hyperspectral setting into the nose of left pod (b): IMU between VNIR and SWIR cameras

Figure 4 .

 4 Figure 4. (a) Hyperspectral setting into the nose of left pod and (b) Inertial Motion Unit (IMU) between VNIR and SWIR cameras.

Figure 5 .

 5 Figure 5. SETHI quad-polarization X+L configuration: Synoptic of sensors and view of in-pod L-band patch array and X-band horn antennas (with optical camera in the foreground).

Figure 6 .

 6 Figure 6. Radar acquisition geometry operated during the NOFO oil-on-water exercise.

Figure 7 .

 7 Figure 7. SETHI -NOFO'2015 experiment -Noise Equivalent Sigma 0 at (a) X-band and (b) L-band.

  The hyperspectral acquisitions (nadir viewing) are made along the first major axis of the racetrack, vertically of the oil-covered area. Side-looking SAR imageries are acquired along the return axis. Trajectories are adjusted before each run of acquisition from the AIS signal of Stril Luna (vessel towing the MOS Sweeper recovering boom and supposed to be within the slick) received in real time onboard the SETHI system. Flying at a ground speed of 120 m.s -1 , the time lags between optical and radar imagery is about a few minutes. A description of each run of remote sensing data acquisition carried out during the MOS Sweeper trial is given Table4below. In this table, the lines in bold correspond to the acquisitions mainly analyzed in the following, namely dual-frequency SAR imagery at 10:01 am UTC (run 8) and VNIR-SWIR hyperspectral imagery at 10:12 am UTC (run 10).

Figure 8 .

 8 Figure 8. Trajectory followed by the SETHI multi-sensors during the NOFO OOW'2015 experiment.

Figure 9 .

 9 Figure 9. (a) SETHI NOFO'2015 (run 10) -HS data over mineral oil -June 09, 2015 -10:12 UTC -Red box shows the location of the ROI -(b) RGB composite of the ROI selected for processing. Colored circles locate the different elements of the image -(c) Spectral reflectance corresponding to the image elements: bright red oil slick, light red oil slick, sheen, seawater and cloud. Vertical gray lines indicate the wavelengths that are used to calculate the indices for oil detection.

  ρ and C ρ correspond to reflectance taken respectively at λA = 1.67, λB = 1.72 and λC = 1.75 nm (see Figure 9 -(c)).

Figure 10 -

 10  shows the results of computing the HI index on the investigated hyperspectral image. The HI score map reveals the center of the oil slick, which corresponds to the bright red region in Figure9 -(b). However, the light red region and the sheen are not detected, as the absorption peak at 1.7 micrometers is visible only for high abundances of oil emulsion.

Figure 11 .

 11 Figure 11. Score maps resulting from spectral index nFI.

  left) shows the histogram of the nFI score map with the peak at 0.024 corresponding to seawater. The definition of four contiguous intervals allows us to generate the image in Figure 12 -(right) in which a different color has been assigned for the three regions of the oil slick (bright red, light red and sheen) and the rest of the image (seawater and clouds).

Figure 12 .

 12 Figure 12. (left) Histogram of the nFI score map with the four colored groups according to different nFI value ranges -(right) Image in which each pixel is affected to the color corresponding to its nFI score group.

Figure 15 .

 15 Figure 15. SETHI NOFO'2015 (run 8) -SAR data over mineral oil -June 09, 2015 -10:01 am UTC -(a) X-band VV-pol and (b) L-band VV-pol -multi-look 7x7 -scale in dB.

Figure 16 .Figure 17 .

 1617 Figure 16. SETHI NOFO'2015 -June 09, 2015 -10:01 am UTC -NPD parameter at (a) X-band and (b) L-bandmulti-look 7x7.

Figure 18 .

 18 Figure 18. SETHI NOFO'2015 -June 09, 2015 -10:01 am UTC -histograms of PR parameter at (a) X-band and (b) L-band over slick-covered (green), slick-free (bleu) and oil recovered areas (red).

  airborne sensor developed by ONERA, is a unique system of imagery designed to explore the science applications of remote sensing. It allows the quasi-simultaneous acquisition of high spatial resolution data in both optical and microwave domains. Up to three different Radar bands can be operated at the same time and each sensor is characterized by a very low instrumental noise floor, not currently available from space. Hyperspectral cameras cover the full range of 0.4-2.5 µm with a very high spectral resolution and one panchromatic camera collects data aligned with the radar observation.Hyperspectral and radar sensors embedded onboard SETHI have been operated during the 2015 oil-on-water exercise where controlled releases of mineral oil have been carried out by NOFO in the North Sea. The acquired dataset is therefore unique because of the overall high resolution, the low noise levels and the quasi-simultaneous acquisitions at L-band, X-band and over the full hyperspectral range. In this paper, dual-frequency SAR data and hyperspectral images have been shown to provide information on the quantity and the nature of oil discharged in oceans. It has also been reported how multi-wavelength remote sensing data can assess the efficiency of the cleanup operations. The work on this dataset has just began with a separate analysis of the potentials of both radar and hyperspectral imagery. The information that they provide are different -the relative concentration and the nature of the oil. However, because the acquisitions are almost simultaneous,

Table 1 .

 1 Environmental Conditions during the MOS Sweeper experiment.

	Date	Time (UTC)	Wind speed at 10 m (m.s -1 )	Wind direction (from-deg)	Significant wave height (m)
	09 June 2015	06:00 09:00 12:00	5 5 7	250 250 250	1 1 1

Table 2 .

 2 Main parameters of optical sensors operated during the NOFO oil-on-water exercise.

		HySpex VNIR1600	HySpex SWIR320m-e Panchromatic CamV2
	Number of pixels	1600	320	7256 x 5462
	Field of view	17°	14°	27°
	Spectral domain	0.4 -1.0 µm	1.0 -2.5 µm	0.4 -0.8 µm
	Spectral sampling	3.6 nm	6 nm	-
	Ground sampling distance (across and along track) at 9 000ft	0.51 x 1.03 m	2.06 x 2.06 m	0.17 x 0.17m
	An Inertial Motion Unit (IMU) iTrace F200 from iMAR, is located close to the two pushbroom
	imaging spectrographs (Figure 4 -(b)). The lever arm between cameras and IMU is small enough to
	ensure accurate measurements of attitude and position. Markers signal from IMU are generated

synchronously with scan lines in order to georeference data in the upcoming processing.

Table 3 .

 3 Radar waveform operated during the NOFO oil-on-water exercise. imagery is made possible by orienting both radar antennas to the same side during operation and sampling/record system high performances. The generation is performed by a 10-bit 2-channel 12 GS/s AWG (Arbitrary Waveform Generator) that transmits a pre-calculated signal. The 150 MHz bandwidth L-band signal (1.25 to 1.4 GHz chirp) is directly generated (no frequency

		SAR X-band	SAR L-band
	Carrier frequency	9.75 GHz	1.325 GHz
	Bandwidth / Slant range resolution	300 MHz / 0.5 m	150 MHz / 1m
	Pulse Repetition Frequency (PRF)	5 000 Hz	5 000 Hz
	Pulse duration	20 µs	20 µs

Table 4 .

 4 Remote

sensing data acquired by SETHI during the MOS Sweeper trial -09 June, 2015 -Data investigated herein appear in bold letters.

Run Time (UTC) Sensor Flight heading (deg) Viewing direction (deg)

	1	09:32	HS VNIR+SWIR	288°	Nadir
	2	09:34	HS VNIR+SWIR	290°	Nadir
	3	09:37	PolSAR X+L	108°	198°
	4	09:43	HS VNIR+SWIR	292°	Nadir
	5	09:46	PolSAR X+L	113°	203°
	6	09:52	HS VNIR+SWIR	294°	Nadir
	7	09:55	PolSAR X+L	113°	203
	8	10:01	PolSAR X+L	293°	023°
	9	10:08	HS VNIR+SWIR	298°	Nadir
	10	10:12	HS VNIR+SWIR	116°	Nadir
	11	10:17	HS VNIR+SWIR	293°	Nadir
	12	10:20	PolSAR X+L	108°	198°
	13	10:24	HS VNIR+SWIR	286°	Nadir
	14	10:27	PolSAR X+L	107°	197°

(http://creativecommons.org/licenses/by/4.0/).

Acknowledgments:

The authors are very grateful to the NOFO (Norwegian Clean Seas Association for Operating Companies) for allowing them to participate in the Oil-on-Water exercise, which was carried out from the 8 th to the 14 th June, 2015. Authors are very thankful for everyone involved in the experiment at sea and colleagues who participated to data processing.

Conflicts of Interest:

The authors declare no conflict of interest.