F Bretar

A Chauve

C Mallet

B Jutzi

MANAGING FULL WAVEFORM LIDAR DATA: A CHALLENGING TASK FOR THE FORTHCOMING YEARS

Keywords: Full waveform, LIDAR, Researches, Data management, Format, Literature review

This paper proposes to summarize researches and new advances in full waveform lidar data. After a description of full waveform lidar systems, we will review different methodologies developed to process the waveforms (modelling, correlation, stacking). Applications on urban and vegetated areas are then presented. The paper ends up with recommendations on future research themes.

INTRODUCTION

Airborne laser scanning (ALS) is an active remote sensing technique providing range measurements between the laser scanner and the Earth topography by the time-of-flight between the emitted and backscattered laser pulse. Direct georeferencing processes turn such distance measurements into 3D point clouds with high accuracy. Even for small footprints, there may be several objects of different range within the beam corridor of the laser pulse that generate individual backscatter returns (echoes). Consequently, commercial systems typically measure first and last pulse and some are able to record up to six echoes for each emitted laser pulse. Due to the 3D structure of natural and artificial objects, the shape of the received pulses may be extremely complex and most ALS systems only provide the coordinates of these scattering objects.

During the last years, a new generation of airborne laser scanners have been developed which are able to record the signal of the entire backscattered laser pulse. These so called full waveform LiDAR systems give more control to the end user in the interpretation process of the physical measurement. Beside the range information, additional information about the structure of the illuminated surfaces can be determined. It has already been shown that full waveform data post-processing shows enormous potentialities for improving forest and urban mapping.

Full waveform systems can bring two important contributions and advantages for additional investigations possibilities. First the processing of the received signal can be used to recover all individual echoes. This enhances the point cloud density with regard to multiple echos (particularly on forest area) as well as the quality of the extracted features within the laser beam corridor. Second by modelling the received waveforms, additional features can be extracted from them. This is for example the amplitudes (also called intensity) and the pulse width derived by the standard deviation of a Gaussian-based decomposition of the waveform. These two values provide mixed information about the geometry and the reflectance properties of the illuminated surface.

Full waveform data is mostly used for forest research since the first experimental devices, with large footprint, were developed for this particular purpose. They produce more detailed description of vegetation structures. It is then possible to estimate, model and infer forest parameters more reliably.

The potentialities of full waveform data are definitively high and the forthcoming years will be particularly important for the development of methodologies, applications and softwares within the international LiDAR community. However, if the management of multiple echos data made appear standard format (e.g. LAS) and software solutions, managing full waveform LiDAR data in terms of efficiency have not been tackled so far. There are three main reasons that could explain this observation: firstly, these data just came out commercially, secondly, their volume is several times larger than multi-echos data and finally, the potentialities of these data have not been turned yet into reality with regard to multi-echo data.

After a brief reminder of the LiDAR systems themselves, we propose in the paper to summarize past researches concerning full waveform LiDAR processing with special emphasis on forest and urban areas. We will then sketch some proposals for a full waveform data format and finally we present new research directions.

BACKGROUND ON FULL WAVEFORM LIDAR SYSTEM

The physical principle of ALS consists in the emission of short laser pulses, with an width of 5-10 ns at full-width-halfmaximum, from an airborne platform with a high temporal repetition rate of up to 200kHz. They provide a high point density and an accurate altimetric description within the diffraction cone. The two way runtime to the Earth surface and back to the sensor is measured. Then the range between the LiDAR system to the illuminated surface is recorded [START_REF] Baltsavias | Airborne Laser Scanning : Basic relations and formulas[END_REF]. The emitted electromagnetic wave interacts with the objects surface depending on its wavelength: first with atmospheric components like particles refraction, absorption or scattering, known to have negligible influence if rain is excluded.

The main influences on the laser light come from artificial or natural objects belonging to the illuminated surface. For ALS systems, near infra-red sensors are used (typical wavelengths from 0.8 to 1.55μm). Pulse repetition rate depends on the acquisition mode and on the flying altitude. Pulse release is done when the previous pulse recording is effective. Full waveform LiDAR systems record the entire signal of the backscattered laser pulse (Figure 1).

Figure 1:

Transmitted and received waveform and the corresponding range in a complex wooded area with a small footprint LiDAR system.

The first operational system Laser Vegetation Imaging Sensor (LVIS), developed by the NASA, appeared in 1999 and demonstrated the value of recording the entire waveform for vegetation analysis [START_REF] Blair | Modeling Laser Altimeter Return Waveform Over Complex Vegetation Using High-Resolution Elevation Data[END_REF]. The first commercial full-waveform LiDAR system was introduced in 2004 [START_REF] Hug | Litemapper-5600 -A Waveform-Digitizing LIDAR Terrain and Vegetation Mapping System[END_REF]. Today, most of LiDAR-involved companies (e.g. Riegl, Optech, Leica, Toposys) propose such an extension to their multiple pulse devices.

Full waveform systems sample the received waveform of the backscattered pulse at a maximum frequency of 1 GHz, which is equivalent to 1 GSamples/s. Such systems differ in sampling rate, in scan pattern and in footprint size. Most commercial systems are small-footprint, typically 0.3 -1 m diameter at 1km altitude, depending on altitude and beam divergence.

To record full waveform LiDAR data, the main commercial manufacturers have added digitization terminals to their systems and increased the storage media capacity. Whatever the LiDAR system method, the constant digitization sampling period varies between 1 to 10 ns. The waveform is not integrally recorded but only for a predefined maximum number of samples. Indeed, it is necessary to avoid recording too many useless samples because they result into massive storage problems. For example, TopoSys ALTM systems can store up to 440 samples for each pulse. This is equivalent to a continuous vertical section of 66 meters (440 × 0.15 m per sample). The TopEye MarkII system saves 128 samples according to a predefined mode which is either "first pulse and later " (127 samples after the first) or "last pulse and earlier " (127 samples before the last detected). This means that full-waveform systems will not record both the echoes from the canopy and from the ground within a given waveform if the trees are taller than the "recording length" of the system.

PROCESSING THE WAVEFORMS

The processing of waveform data starts in maximizing the number of relevant detected peaks within each signal.

A first approach consists in deriving a parametric formulation of the received signal. From the local maxima of the fitted function the range value is calculated and 3D points can be determined. Pulse properties (width and amplitude) can be calculated at the same time. Here, waveform processing consists in decomposing the signal f(x) into a sum of components f j (x) so as to characterise the different objects along the path of the laser beam:

Considered as a sum of Gaussian functions initially in [START_REF] Hofton | Decomposition of laser altimeter waveforms[END_REF] and [START_REF] Wagner | Gaussian decomposition and calibration of a novel smallfootprint full-waveform digitising airborne laser scanner[END_REF], various formulations of f j ( x The authors show that the waveform modelling is better using a GG function. More over, it introduces another pulse feature (α) that can be integrated in a segmentation process (Section 4.2).

An other approach consists in applying signal processing methods based on the transmitted and the received waveform.

The matched filter is computed by the normalized crosscorrelation function R sr between the transmitted waveform s(t) of the emitted pulse and the received waveform r(t) of the backscattered pulse.

Here, echoes are local maxima of the correlation functions (Hofton & Blair, 2002;[START_REF] Kirchhof | Iterative processing of laser scanning data by full waveform analysis[END_REF] .

This approach considers strong echoes for further processing, but weak echoes have to be detected, revised and processed again. In order to overcome this problem and retrieve partially occluded objects, waveforms can no longer be processed independently: local neighbourhoods have to be introduced. [START_REF] Stilla | Detection of weak laser pulses by Full Waveform stacking[END_REF] propose a waveform stacking strategy of several weak echoes within a local environment to increase the signal-to-noise ratio.

Alternatively, the knowledge of the local geometry can be used to improve the cross-correlation techniques. Considering the waveform as a convolution between the transmitted waveform and the surface response (which is unknown at first), the crosscorrelation is performed by neglecting the surface response. [START_REF] Kirchhof | Iterative processing of laser scanning data by full waveform analysis[END_REF] have designed an iterative process to refine the matched filter in so as to integrate a modelled planar response to the matched filter, assuming that laser beam hits a planar surface with a given slope. The process consists in iteratively estimating the objects surface response to first improve range value estimation and discriminate between partially penetrable objects and impenetrable ones, then to extract and select surface primitives in 3D points expected to belong to impenetrable surfaces. Finally the authors estimate the relative surface slope (RANSAC) and compute the surface response, used as prior knowledge in a new step.

APPLICATIONS

Forest areas

Many studies have already been carried out for estimating forest parameters using multiple echo data derived from laser scanning systems: small footprint LiDAR systems, with high point density, can be used to extract trees in small areas [START_REF] Brandtberg | Detection and analysis of individual leaf-off tree crowns in small footprint, high sampling density LiDAR data from the eastern deciduous forest in North America[END_REF], their height and crown diameter [START_REF] Persson | Detecting and measuring individual trees using an airborne laser scanner[END_REF], their volume [START_REF] Naesset | Estimating tree heights and number of stems in young forest stands using airborne laser scanner data[END_REF], to classify them according to species [START_REF] Holmgren | Identifying species of individual trees using airborne laser scanner[END_REF], estimate their particular characteristics [START_REF] Andersen | Estimating forest canopy fuel parameters using LIDAR data[END_REF] and even to measure the growth of the forest and detect trees that have been felled [START_REF] Yu | Automatic Detection of Harvested Trees and Determination of Forest Growth Using Airborne Laser Scanning[END_REF]. Large footprint systems can reach the ground and the tree tops in dense, tall canopies. Woodland parameters can be estimated at large scale: density of population, coverage, biomass, etc. [START_REF] Means | Use of large-footprint scanning airborne LiDAR to estimate forest stand characteristics in the western cascades of Oregon[END_REF].

Many algorithms have been developed for forest measurements [START_REF] Hyyppä | Algorithms and Methods of Airborne Laser-Scanning for Forest Measurements[END_REF]. Full-waveform experimental systems with large footprint developed by the NASA have been successfully used in forest environments for measuring the canopy height [START_REF] Lefsky | LiDAR remote sensing of the canopy structure and biophysical properties of Douglas-fir western hemlock forests[END_REF] or the vertical distribution of canopy material [START_REF] Dubayah | LiDAR Remote Sensing for Forestry Applications[END_REF].

Furthermore, the modelling of raw LiDAR signal recorded by recent small footprint industrial systems has already proved efficient in increasing the number of detected objects in comparison with data provided by multi-echo LiDAR systems for which real-time point extraction method is unknown to the end user [START_REF] Persson | Visualization and Analysis of Full-Waveform Airborne Laser Scanner Data[END_REF]Chauve et al., 2007). For deciduous vegetation, measurements in leaf-off conditions give a direct impression of the internal structure of the canopy (the main levels of vegetation) and are very useful to estimate the real "shape" of the trees [START_REF] Reitberger | Analysis of fullwaveform LiDAR data for tree species classification[END_REF].

In forested areas, waveforms can be composed of weak returns from both the top of the canopy and the ground, and also of distributed backscatters from the different layers of the vegetation, which leads to groups of overlapping echoes. Hardware detection algorithms, most of the time using thresholds, can hardly detect or separate such very low peaks or groups of echoes (Figure 1). Therefore, processing the waveforms allows to control the point extraction method and to make it more efficient: an improved peak detection was shown to be very successful to extract additional objects in the received waveforms and to detect from 30% to 130% additional points depending on vegetation density (Chauve et al, 2007b). These points are located mainly within the canopy and in highly dense understory (Figure 2). Detecting such weak echoes allows a better describtion of the 3D vegetation structure and the ground. As a consequence, Digital Terrain Model (DTM), Digital Surface Model (DSM), and the derived Canopy Height Model (CHM) are expected to be significantly improved, but is still to be validated with reference data.

Urban areas

When processing full waveform data on urban areas, one can observe that the point density is barely improved. Thanks to waveform analysis, more echoes are found in tree canopies, whereas LiDAR pulses cannot penetrate rigid, opaque structures such as buildings and streets. Consequently, a single peak is still present within the waveform and can be detected by signal processing algorithms. Multiple echoes can appear on building edges and superstructures. Indeed, post-processing algorithms enable to extract weak echoes not found by on-line detection techniques. In urban areas, it is observed when the laser beam hits for instance building edges. The resulting waveform is therefore composed of distributed backscatters of the roof and the ground, which can often not be separated by hardware detection algorithm using fixed thresholds.

Modelling LiDAR waveforms permits to extract 3D point clouds featuring supplementary useful parameters in addition to the traditional (x, y, z) coordinates and to perform subsequently point cloud segmentation based on these parameters. The standard features are range, amplitude and width. Nevertheless, analysing a 3D point cloud processed from full waveform data with model parameters does not allow to define particular behaviour for each urban object. The echo is wider on the canopy with regard to roads or grass ones but a wide echo with low amplitude does not necessarily comes from vegetation. High amplitudes are noticed on grass and asphalt and variable amplitude on the roofs of buildings, depending on the roof materials. In fact, roads and building roofs are made from different types of material and, therefore, have different similar values to natural objects [START_REF] Gross | Segmentation of Tree Regions using Data of a Full-Waveform Laser[END_REF]. Moreover, it is visually possible to distinguish between different urban materials hit by a laser beam but a more in-depth processing is required to recognise roof materials [START_REF] Jutzi | Laser pulse analysis for reconstruction and classification of urban objects[END_REF].

Consequently, simple algorithms aiming at segmenting building and vegetated areas, e.g., decision tree approach with empirical thresholds, lead to a certain rate of incorrect classification [START_REF] Duong | Full waveform analysis: ICESAT laser data for land cover classification[END_REF][START_REF] Ducic | 3D Vegetation mapping and classification using fullwaveform laser scanning[END_REF]. A more reliable approach to detect vegetated areas is proposed in [START_REF] Gross | Segmentation of Tree Regions using Data of a Full-Waveform Laser[END_REF], based on the eigenvalues of the covariance matrix computed for each point with the intensity values in a cylindrical and spherical environment. Another method based on a supervised classifier (SVM) has been developed [START_REF] Mallet | Analysis of fullwaveform LiDAR data for an accurate classification of urban areas[END_REF]. It provides a suitable segmentation of ground, building and vegetation areas but requires classical geometric features in addition to parameters extracted for waveform modelling.

To achieve more advanced point segmentation in urban areas and to understand how the pulse interacts with the surface, a theoretical knowledge of the influence of the geometric and radiometric properties of the illuminated surface (i.e., the differential laser cross-section) on the shape of the waveforms is required. Quantifying specifically both geometric and radiometric influence of a object on the received waveform is undoubtedly the most promising line of research for that purpose.

FUTURE RESEARCH DIRECTIONS

Researches on full waveform data are still at their beginnings, but seem very promising in terms of automatic data extraction. However, if the technology seems well managed by manufacturers, many questions will arise for future work.

Whatever the scenario type will be, if it is clear that full waveform LiDAR data provide range information, it is still expected to be fully proved that they contain physical assessments of the backscattering properties of the illuminated surface. Among other interesting points, two of them seem essential and have to be investigated in priority:

• The influence of the surface geometry and radiometry onto the shape of the waveform.

•

The influence of the lasers wavelength onto the measurement itself.

Answering these points needs a modelling step that could be based on a ray-tracing model or even more complex, a electromagnetic model, which bases on the wave characteristic of the light, e.g. to investigate speckle effects. Such approaches will consider the LiDAR footprint size, the beam divergence, the sensor FOV, the wavelength, the direction of the laser beam propagation etc., but also the characteristics of the scenario (tree species, building geometry...). It also necessitates an experimental step to build a data base of optical responses in various optical wavelength over various features.

Furthermore, most of topographic LiDAR systems work with a single wavelength. The recording of several waveforms, each of them emitted at a different wavelength could enhance the object description. These so called hyperspectral laser systems could deliver for instance multiple intensity information of surfaces instead of only monochromatic information. Finally, scientists of the ISPRS community would take high benefit of having a better knowledge of commercial LiDAR systems, particularly of its system specifications, which is usually different for each sensor and most of the relevant information is kept secret by the manufacturers.

The extensive use of full waveform LiDAR data leads to think of a new data format and data management system as LAS format and TerraScan© for point clouds. The format could be based on a multiple layer structure in the sensor geometry, each of them linked to the others by pointer arrays (Figure 3). Among important layers, there are a raw data layer containing all waveforms, a georeferencing layer containing the trajectory and sensor information interpolated at each measurement and a modelling layer containing the parameters of the analytic description of the waveform. A XML meta file could describe each layer. An orthorectified geometry should be generated in terms of GUI and linked to the sensor geometry.

CONCLUSION

We have presented in this article the main lines of researches on full waveform LiDAR data performed, especially in the ISPRS community. Even if these data have been used for only a short time now, it seems that fruitful and promising results have come out in the recent years. Many problems have to be sorted out before using these data as multi-echo LiDAR data are. Nevertheless, the tasks are extremely challenging since it is a new and wide research area, which begin to deals with physical remote sensing.

  ) have been tested in Chauve et al. (2007a): the Gaussian function (G), the Log normal function (LG) and the Generalized Gaussian function (GG).

Figure 2 :

 2 Figure 2: Profil of LiDAR points from multiple pulses data (red)and from full waveform data (green).

Figure 3 :

 3 Figure 3: Sketch of the proposed data format for full waveform LiDAR data.


Clément Mallet

Uwe Soergel
email: soergel@ipi.uni-hannover.de

Frédéric Bretar

ANALYSIS OF FULL-WAVEFORM LIDAR DATA FOR CLASSIFICATION OF URBAN AREAS

Keywords: lidar, signal processing, waveform analysis, modelling, classification, urban

In contrast to conventional airborne multi-echo laser scanner systems, full-waveform (FW) lidar systems are able to record the entire emitted and backscattered signal of each laser pulse. Instead of clouds of individual 3D points, FW devices provide connected 1D profiles of the 3D scene, which contain more detailed and additional information about the structure of the illuminated surfaces. This paper is focused on the analysis of FW data in urban areas. The problem of modelling FW lidar signals is first tackled. The standard method assumes the waveform to be the superposition of signal contributions of each scattering object in such a laser beam, which are approximated by Gaussian distributions. This model is suitable in many cases, especially in vegetated terrain. However, since it is not tailored to urban waveforms, the generalized Gaussian model is selected instead here. Then, a pattern recognition method for urban area classification is proposed. A supervised method using Support Vector Machines is performed on the FW point cloud based on the parameters extracted from the post-processing step. Results show that it is possible to partition urban areas in building, vegetation, natural ground and artificial ground regions with high accuracy using only lidar waveforms.

INTRODUCTION

In the last decade, airborne lidar systems have become an alternative source for acquisition of altimetrer data. Such devices deliver a reliable, fast and accurate representation of terrestrial landscapes through georeferenced and unstructured 3D point clouds (RMSE < 0.1 m in altimetry). Range is determined directly according to the signal runtime measurement whereas photogrammetric techniques derive the 3D information indirectly based on the camera orientations and the disparity of correspondences in stereo photos identified by image matching methods. A large body of literature addresses the potential of laser scanning data for urban and suburban area analysis. For instance, many algorithms for classifying lidar point clouds have been developed so far aiming at building detection and subsequent reconstruction [START_REF] Matikainen | Automatic Detection of Buildings from Laser Scanner Data for Map Updating[END_REF][START_REF] Sithole | Experimental comparison of filter algorithms for bare-Earth extraction from airborne laser scanning point clouds[END_REF]. They often depend on the availability of a cadastral map, even if, without this information, building outlines can at least roughly be extracted. In the latter case the discrimination of buildings from adjacent trees is difficult. All these approaches rely only upon geometric and topologic criteria and have in common to be sensitive to large off-terrain objects and surface discontinuities. Therefore, many authors proposed other inputs like echo intensity [START_REF] Tóvári | Classification methods for 3D objects in laserscanning data[END_REF] or multi-spectral images [START_REF] Rottensteiner | Using the Dempster-Shafer method for the fusion of LIDAR data and multi-spectral images for building detection[END_REF] to achieve better results. Since few years, a new generation of lidar devices designed to digitize and to record the entire backscattered signal of each emitted laser pulse became operational. They are called full-waveform (FW) lidar systems. Full-waveform data offer the opportunity to overcome many drawbacks of classical multi-echo lidar data [START_REF] Wagner | From single-pulse to full-waveform airborne laser scanners: Potential and practical challenges[END_REF]. In addition to single range measurements, further physical properties of the objects included in the diffraction cone may be revealed by analysis of the shape of the sampled backscatter sequence. Many studies have already been carried out to perform FW data processing, mainly in vegetated areas. The higher point density inside the penetrated canopy offers insight in the vegetation types and state [START_REF] Harding | Laser altimeter canopy heigh profiles. Methods and validation for closed-canopy, broadleaf forests[END_REF]. In urban areas, the potential of such data has been barely investigated. For instance, [START_REF] Jutzi | Waveform processing of laser pulses for reconstruction of surfaces in urban areas[END_REF] extract linear features on roof tops by establishing neighbourhood relationships between waveforms. They also aim at localizing more accurate building outlines. On the other hand, by exploiting other features in addition to the geometry (e.g., pulse amplitude or width), segmentation of vegetated areas is now possible [START_REF] Gross | Segmentation of tree regions using data of a full-waveform laser[END_REF]. To achieve more advanced point classification in urban areas, a theoretical knowledge of the influence of the geometric and radiometric properties of the hit targets (i.e., the differential laser cross-section) on the shape of the lidar waveforms is required. The aim of the article is to show that a fine analysis of fullwaveform lidar data can lead to an accurate classification of urban areas. The general outline of this work is described in Section 2. Then, a new modelling function is proposed to process raw signals in Section 3. The results of the integration of the previous extracted features into a supervised classification algorithm are presented in Section 4. The aim is to discriminate four classes: buildings, vegetation, artificial and natural ground regions. The test data sets are outlined in Section 5. Finally, the results of waveform processing and classification are presented and the conclusions are finally drawn.

OVERALL METHODOLOGY

Common laser data formats are clouds of 3D points, often provided without link to the original laser shot. In contrast to this, FW profiles comprise information of the 1D object structure along the line of sight. Nevertheless, such data are more difficult to handle and specific studies have to be carried out. In this article an approach is proposed to process FW lidar data to extract 3D point clouds featuring more useful parameters in addition to the traditional (x, y, z) coordinates and to perform subsequently a point classification based on these parameters.

Waveform processing consists in decomposing the waveform into a sum of components or echoes, in order to characterise the different individual targets along the path of the laser beam. A parametric approach is chosen, i.e., parameters of a mathematical model are estimated. The aim of waveform processing is to maximize the detection rate of relevant peaks in order to foster information extraction from the raw signal. Non-parametric methods like splines, neural networks or Parzen windows are known to work very well to fit 1D signals. Nevertheless, they eventually approximate curves to the data [START_REF] Bishop | Pattern Recognition and Machine Learning[END_REF] and do not provide the signal maxima location, which is required to produce 3D point clouds through a georeferencing process. The objective of waveform processing is twofold. A parametric decomposition increases the accuracy of the signal maxima along the lidar beam. Furthermore, from a class of functions the best fit to the waveform is chosen. This allows to introduce new parameters for each echo and to extract additional information about the target shape and its reflectance. Then, the extracted point cloud is classified. The aim is to assess whether or not each new feature introduced is relevant for classification and how significant it is for urban analysis (does it provide really useful information?). The features are fed into a supervised classification algorithm using Support Vector Machines (SVM). This method is well adapted to deal with highdimensional feature space since the algorithm complexity does not depend on the data dimension. Furthermore, SVM belong to the non-parametric classification techniques, i.e., no parametric probability density functions are required. In recent years, SVM became relevant for solving remote sensing classification tasks [START_REF] Huang | An assessment of support vector machines for land cover classification[END_REF]. SVM allows to use jointly classical geometric features (number of extracted peaks, altitude difference between first and last echoes of a waveform, etc.), image-based information [START_REF] Secord | Tree Detection in Urban Regions Using Aerial LiDAR and Image Data[END_REF] as well as in our case new parameters extracted from the post-processing step. The methodology for classification in urban areas by FW lidar data analysis is designed to be flexible. Depending on the modelling function, the theoretical understanding of pulse propagation in such regions and the chosen options of the SVM classifier, it is possible to adjust the level of detail of the classification.

WAVEFORM PROCESSING

Methodology

Our methodology is based on a paper written by [START_REF] Chauve | Processing fullwaveform lidar data: modelling raw signals[END_REF]. The authors describe an iterative waveform processing using a Non-Linear Least Squares fitting algorithm. After coarse initial peak detection, missing peaks are found in the residuals of the difference between the modelled and initial signals. If new peaks are detected, the fit is performed again. This process is repeated until no further improvement is possible. This enhanced peak detection method is useful to model complex waveforms with overlapping echoes and also to extract weak echoes not found by on-line detection techniques. In urban areas, the former case is observed when the laser beam graces building edges. The resulting waveform is therefore composed of distributed backscatters of the roof and the ground, which can often not be separated by hardware detection algorithm using thresholds. Moreover, the methodology has been improved to take the 'ringing effect' into account: after the sampled emitted pulse, a small secondary maximum due to the effects of the hardware waveform processing chain can be seen. Consequently, in urban areas, when the laser beam hits plane objects of high reflectance and with a small angle of incidence (typically streets and roofs), such artifact is still present in the reflected waveform. It is typical of FW sensors and does not exist in multiple-pulse point clouds. In the iterative process, a weak pulse just behind a strong one is now removed when their amplitude ratio is closed to the ratio computed from the emitted pulse (given with the data).

Modelling functions

Waveforms collected with a small-footprint lidar system are used in this article (RIEGL LMS-Q560). Such data can be well modelled by superposition of Gaussian pulses [START_REF] Hofton | Decomposition of Laser Altimeter Waveforms[END_REF]. [START_REF] Wagner | Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner[END_REF] have shown that more than 98% of the observed waveforms collected from the RIEGL system could be fitted with a sum of Gaussian functions. Each laser output pulse shape is assumed to be Gaussian, with a specific and calibrated width. The collected pulse is therefore a convolution between this distribution and a "surface" function, depending on the reflecting objects. Nevertheless, in fact the transmitted signal is not always Gaussian. Indeed, it is observed that the LMS-Q560 transmitted waveform is slightly asymmetric. In urban areas, most of the return waveforms are in reality subject to the mixed effects of geometric (e.g., roof slopes) and radiometric object properties (e.g., different kinds of streets and roof materials), histograms of the four considered classes are illustrated in figure 1. Hence, the characteristics of return peaks may differ significantly. It was already shown that standard extensions of the Gaussians model, which are Lognormal and generalized Gaussian functions, are suitable to model raw lidar signals. Using the generalized Gaussian (GG) model improves the signal fitting for symmetric and distorted waveform shapes (more than 99.3% of satisfactory results) [START_REF] Chauve | Processing fullwaveform lidar data: modelling raw signals[END_REF]. Here, the GG model was used also to process two FW data sets of different sites:

fGG(x) = A exp - |x -µ| α 2 2σ 2 ! ( 1
)
where A is the pulse amplitude, σ its width, µ the function mode and α a shape parameter which allows to simulate Gaussian (α = √ 2), flattened (α > √ 2) or peaked (1 ≤ α < √ 2) pulses. A, σ and α are the three new features which will be introduced in the classification step in Section 4. The Lognormal model fits asymmetric pulses with success but fails for symmetric ones.

CLASSIFICATION OF URBAN AREAS

Methodology

Based on a clustering analysis of the parameters extracted from the modelling step, four classes have been chosen to character-ize urban areas: buildings, vegetation, natural ground and artificial ground. Artificial ground gathers all kinds of streets (tar, asphalt,. . . ) and pavements whereas the natural ground class includes grass, sand and bare-earth regions.

Support Vector Machines

The general mathematical formulation of SVMs is briefly recalled in this section. The SVM method consists in finding the hyperplane maximizing the distance (called the margin) to the closest training data points in both classes (the support vectors). For a linear classifier, ω(x) = w • xθ, where w ∈ D is the normal vector to the hyperplane and θ the bias. We aim at finding the classifier parameters (w, θ) which verify:

Linear SVMs

∀(xi, yi) ∈ A, yi × (w • xi -θ) > 0 (2)
Since the SVM method searches the best classifier (i.e., the largest margin), we impose:

∀(xi, yi) ∈ A, yi × (w • xi -θ) ≥ 1 (3)
The support vectors lie on two hyperplanes w • xθ = ±1 which are parallel and equidistant to the optimal linear separable hyperplane. Finally, the optimal hyperplane has to maximize the margin (i.e., the Euclidian distance between both hyperplanes, defined as 2/ w ) under the constraints defined in Equation 3. Unfortunately, in most cases, such quadratic optimization problem is unsolvable: we cannot find a linear classifier consistent with the training set. The classification problem is not linearly separable.

Consequently, slack variables1 ξi are introduced to cope with misclassified samples and prevent Equation 3 from being violated. Another reason is the avoidance of over-fitting the classifier to the training samples, which would result in poor performance. It becomes:

∀(xi, yi) ∈ A, yi × (w • xi -θ) > 1 -ξi / ∀i ∈ [1, N ], ξi ≥ 0 (4)
The final optimization problem is subsequently:

min " w 2 2 + C N X i=1 ξi # subject to (4) (5)
C is a constant which determines the trade-off between margin maximization and training error minimization.

Nonlinear SVMs When the classification problem is not linearly separable, one solution consists in changing the feature space.

The data is projected in a higher dimension space using a nonlinear mapping function Φ : D → H, in which the new distribution of samples enables the fitting of a linear hyperplane. Kernels methods provide nonlinear hyperplanes and improve classification abilities. The same margin optimization method can then be performed.

Finding Φ is a difficult problem. In practise, the xi points are implicitly projected in H by defining a kernel K :

D × D → R with K(xi, xj) = (Φ(xi) | Φ(xj)).
In fact, the knowledge of K is sufficient to compute the optimal classifier. It has only to fullfil Mercer's condition [START_REF] Schölkopf | Advances in Kernel Methods. Support Vector Learning[END_REF].

Multi-class SVMs

SVMs are designed to solve binary problems. When having n ≥ 3 classes of interest, various approaches are possible to address the problem, usually combining a set of binary classifiers. We choose the 'one-against-one' approach because it has been shown to be more suitable for large problems [START_REF] Hsu | A comparison of methods for multi-class Support Vector Machines[END_REF]. For such pairwise classification, n (n-1) 2 binary classifiers are computed on each pair of classes. Each sample is assigned to the class getting the highest number of votes.

A vote for a given class is defined as a classifier assigning the sample to that class.

In practise

The LIBSVM software is used to implement the SVM algorithm (available at http://www.csie.ntu.edu.tw/ ∼ cjlin/libsvm). Slack variables are introduced (soft-margin classifier). Then, the parameter C has to be optimized with the kernel hyperparameters (see Section 4.3).

Kernel selection

Without sufficient a priori knowledge of the influence of geometric and radiometric parameters on the pulse shape (or even strong hints about characteristic behaviours on urban areas), the design of a kernel dedicated to our specific purpose given our cues is a very difficult task. Therefore, a generic kernel was selected, the Gaussian kernel defined as:

K(xi, xj) = exp " xi -xj 2 2γ 2 « with γ > 0 (6)
where γ tunes how similar to the training data the test data is expected to be (γ → 0 for instance leads to over-fitting and consequently reveals a low generalization ability of the classifier).

Because optimal values of C and are not known beforehand, a grid search is performed in which the cross-validation accuracy (CVA) is computed for each point. In a v-fold cross-validation procedure, the training data are divided in v subsets of equal size. The classifier is trained on v -1 subsets and ran on the remaining one. The CVA represents the percentage of samples correctly classified averaged over all the subsets when they were used as the testing subset. The (C,γ) grid is composed of exponentially growing values of C and γ, for instance, in our study C, γ = 2 -15 , 2 -13 ,. . . , 2 15 . After the coarse grid search, a finer one is computed in a smaller range around the optimal parameters found in the first step. Such grid search is necessary since the CVA over (C,γ) set is not convex.

Feature selection and relevancy

Our feature vector for each lidar point has eight components.

• ∆r: difference between the pulse range and the highest range (lowest altitude) found in a large spherical environment (20m radius for instance), • R: residuals computed from a plane estimated by a robust L-estimators with norm L1.2 (p=1.2 is proved to be the optimal value for the Lp estimator, see [START_REF] Xu | Epipolar Geometry in stereo, motion and object recognition[END_REF] for more details) on the points in a given neighbourhood (here a spherical environment of 0.5 m radius), • nz: the deviation of the local normal vector from the vertical, • ∆z f l : the altitude difference between the first and the last pulse of the waveform, • N : the number of echoes in the waveform,

• A, σ, α: the pulse amplitude, width, and shape respectively (extracted from the waveform processing step described in Section 3). The three first parameters can be used with every 3D point cloud (only geometric information). The three last ones are derived by waveform modelling (amplitude can also be available with multiple-pulse point clouds). Feature ∆r allows to filter points on the terrain from off-ground points; ∆z f l and N discriminate vegetation points from the others. These two information are necessary because the number of echoes alone is not sufficient. Multiple reflections can occur when the laser beam hits a roof (due to superstructures) and the street (due to cars or building edges). R and nz values are also affected by such data. The generalized Gaussian parameters are introduced in the SVMs to see how significant they are for the segmentation between the four classes and especially natural and artificial grounds. Table 1 summarizes the feature values for the different labels. Other features have been tested such as the altimetric texture and several moments of the three extracted parameters in a given neighbourhood (mean, standard deviation, and skewness) and the backscatter cross-section [START_REF] Wagner | Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner[END_REF] but they were not found relevant for our study.

↓ Feature Building Veget.

Art. grd Nat. grd

∆r variable variable → 0 → 0 R → 0 high → 0 → 0 nz [-45,45 o ] variable [-10,10 o ] [-10,10 o ] ∆z f l 0 high 0 0 N ∼1 ≥ 1 1 1 A variable medium low variable σ medium high variable variable α [1.5,1.6] variable ≃ √ 2 > √ 2
Table 1: Empirical values of the selected features for SVM classification for the four labels (Veget.: vegetation, Art. grd: artificial ground, Nat. grd: natural ground).

FULL-WAVEFORM LIDAR DATA

Two data sets are available for this study. The data acquisitions were performed respectively in September 2006 and May 2007 with the RIEGL LMS-Q560 system over the cities of Biberach (Germany) and Le Brusquet (France). The main technical characteristics of this sensor are presented in [START_REF] Wagner | Gaussian decomposition and calibration of a novel small-footprint full-waveform digitising airborne laser scanner[END_REF]. The specifications of each survey are described in Table 2. RIEGL full-waveform system allows to determine the vertical distribution of targets within the diffraction cone with a temporal sampling of 1 ns. Each return waveform is composed of one or two sequences of 60 and 80 samples (for Biberach and Le Brusquet, respectively).

For each recorded waveform, the digitized emitted pulse and the echoes found by the hardware detection algorithm are given as well as their amplitude and width. In urban areas, the digitization of vertical sections of around 30m is sufficient to record backscattered signals both from the tree tops and the ground below them. The city of Biberach includes residential, industrial and dense urban areas. The surveyed area of Le Brusquet consists of scattered houses in an alpine rural region.

RESULTS AND DISCUSSION

Modelling raw signals

As described in details in [START_REF] Chauve | Processing fullwaveform lidar data: modelling raw signals[END_REF] more crucial in urban areas. Indeed, the benefits of full-waveform data for building reconstruction or classification are threefold.

First, the GG model improves signal fitting. More relevant points are extracted. 5% additional pulses are found which correspond to weak pulses in hedges, building edges and roof superstructures. Furthermore, taking the 'ringing effect' into account allows to exclude artifacts (i.e., non-existing points) during post-processing (see Figure 2). On ground and building regions, ringing points are removed (> 15% of the total number of points). Furthermore, decomposing parametrically the waveforms increases the accuracy of the signal maxima location along the lidar beam.

The target range detection is subsequently improved by more than 0.05 m on building roofs and ground.

Finally and above all, the global fitting quality is increased, be- The same data but after removal of artifacts (right). The roof appears no longer doubled.

cause flattened, narrow, and high pulses are now well detected.

Figure 1 shows that since the α values are in many cases larger than √ 2 (mean value=1.52), waveforms are in reality flattened compared to Gaussian curves. Depending on the application, the Gaussian model can nevertheless be sufficient. For example, in forested areas, waveforms are mainly composed of at least two peaks. In such application, it is often not of interest to extract a shape parameter, which will depend both on the reflected target and on the targets already hit by the laser beam. But, in urban areas, the GG contribution is all the more significant since this parameter provides a genuine information about the target shape and reflectance.

Behaviour of extracted parameters

A morphological analysis of lidar waveforms is needed and a simulation step is required to understand how the pulse interacts with the targets and to decorrelate geometric and radiometric influences. Amplitude and width values have also to be corrected according to the waveform angle of incidence and the target slope. Analysis of the extracted point clouds revealed the following general behaviour of the three extracted parameters for different targets in urban areas:

• High amplitude values are found on building roofs, independent of the material (except metal), on gravel, on sand and cars. The lowest values correspond to vegetation points due to a higher target heterogeneity and attenuation. Asphalt and tar streets have also low amplitude values, but despite low contrast it is possible to visually discriminate different kinds of surfaces. • Vegetation spreads lidar pulses that is why the highest width values are found in trees and hedges. Ground and building surfaces coincide with low width values even if an increasing roof slope tends to increase pulse width. • Very low and high shape values are characteristics of building edges and vegetation. Building region corresponds with α values in a specific range (between 1.5 and 1.6). Natural ground (especially grass) and artificial ground surfaces can also be visually distinguished. However, vegetated areas exhibit comparable values (see Figure 1).

Classification

Both data sets have been classified. Approximatively 0.8% of the pulses were used for the training step and 1% to find the optimal values of C and γ. For all the tests carried out, the correct classification rate for the training step oscillates between 80 and 90%.

It illustrates that the SVM classifier does not over-fit, but is able to generalize and has been trained sufficiently.

Table 3 gives the classification results over the city of Biberach using the vector composed of eight features. It shows that the segmentation between different kinds of ground leads to a certain rate of misclassification. The main reasons are, first, that only few grass or sand regions are present in Biberach area and therefore only limited numbers of samples are available for training and test. Moreover, the clusters in the feature space of these two classes are very close (see table 1). The results are therefore very sensitive to the training step and the selected regions. Consequently, the SVM classification often fails when discriminating these two regions. Nevertheless, tests carried out on the city of Le Brusquet (rural area) show that classification in four labels is still conceivable when enough training samples are available (Figure 4). The building and vegetation points are well classified.

As expected, some building points are classified as ground (their values can be close e.g., a flat dark roof close to the ground) and as vegetation especially superstructure and building edge points. Vegetated points can also be labelled as building when the laser beam hits dense tree areas. The Overall Accuracy is used as a quality criterion and is defined as:

ρ = P dimY i=1 Ai,i P dimY i=1 P dimY j=1 Ai,j ∈ [0,1] (7)
where Ai,j gives the number of laser points labelled as j and belonging to the class i in reality. ρ is equal to 1 when the classification is perfect and 1/dim Y when the classifier randomly chooses the class for each point with the same probability. Figure 3 shows the evolution of the classification accuracy depending on the input features, adding them by their historical 'order of appearance'

CONCLUSIONS AND PERSPECTIVES

A flexible methodology for full-waveform lidar data analysis and classification in urban areas has been proposed in this article. In a first part, it has been shown that modelling accurately waveforms improves signal fitting and provides point clouds with additional useful parameters. Such parameters are physically interpretable and significantly contribute to an appropriate classification algorithm. The main limitation is that the parametric expression of the waveform functions has no longer simple formulation and new algorithms are needed to perform the optimization step. The Reversible Jump Markov Chain Monte-Carlo (RJMCMC) technique is one of them and will be soon used to handle more complex modelling functions.

In a second part, we can conclude that the SVM is a suitable methodology to perform classification in urban areas since it can handle classical geometric features like the 3D coordinates together with new features extracted from the waveform processing step. First results are promising; discrimination of buildings, vegetation, and ground regions was achieved with 92% accuracy in dense urban areas. Segmentation of different kind of surfaces is also possible. Similar accuracies have been reported for instance in [START_REF] Matikainen | Automatic Detection of Buildings from Laser Scanner Data for Map Updating[END_REF], with only multi-echo lidar data. Classification with features used in such paper and FW features has to be performed to assess the real contribution of full-waveform lidar data. Many improvements are conceivable with regards to the results. First, other generic SVM kernels have to be tested. On the other hand, a specific kernel can be formulated dedicated our specific task. For that purpose, the number of features has to be reduced and therefore synthetic cues found. Another solution is perhaps to iteratively process SVM classification focusing at each step on a specific class and segment it more precisely. A third possibility is eventually to skip the step of feature choice and to use the vectors of the FW data instead. Finally, the classification results shall be the foundation of higherlevel reasoning aiming at the 3D reconstruction of buildings. For this purpose, geometric and topologic object features will be modelled, which are required for instance for object grouping.

Figure 1 :

 1 Figure 1: Histogram of α values over the four test classes.

  D is the data space, Y the label space and A the training set (e.g., D = R n , Y = {-1, 1} in two-class problem). The classification is carried out using a linear discriminant function ω (D → Y). xi ∈ A are the N training samples available with their labels yi / i ∈ [1, N ]. The theoretical aim of supervised classification is to find a classifier consistent with the training set.

Figure 2 :

 2 Figure 2: Building point cloud without taking the 'ringing effect' into account (left, the black arrow shows the false point layer.). The same data but after removal of artifacts (right). The roof appears no longer doubled.

Figure 3 :

 3 Figure 3: Overall Accuracy evolution depending on the features included in the SVM algorithm. Starting from the vector {µ∆r; R; nz}, the other ones are added progressively (Biberach area).(see part 4.4). Each new feature improves the classification results. A label-by-label analysis reveals that the amplitude value allows to discriminate building and ground points; the feature σ is helpful to enhance the building/vegetation separation. In reality, results are slightly worse for ground points with α than without the integration of this parameter for the Biberach data sets (63.3% success without α for the natural ground class), whereas this parameter visually improves the results over Le Brusquet (see figure 4, no ground truth available for this area). Another solution has to be found to discriminate ground surfaces better. The figures 4 and 5 give examples of classified point over the two

Figure 5 :

 5 Figure 5: Classification results in a dense urban area (Biberach city). Left: orthoimage of the region of interest. Right: classified point cloud (yellow: buildings, red: vegetation, blue: artificial ground and green: natural ground).

Table 2 :

 2 Overview of the specification of the data sets.

	Area	Biberach Le Brusquet
	Urban specificity	dense	rural
	Flight height (m)	500	700
	Footprint size (m)	0.25	0.35
	PRF (kHz)	100	111
	Pulse width (ns) Temporal sampling (ns)	≥ 5 1
	Vertical section (m)	18 or 36	24 or 48
	Pulse density (/m 2 )	2.5	5

, it is still appropriate to model complex waveforms with the GG function and all the

Table 3 :

 3 Confusion matrix computed with ground truth consisting of 6% of the whole data set of Biberach (ρ = 0.81 and 135627 points).

	Area (number of	Buildings Vege-	Art.	Nat.
	reference points)		tation ground ground
	Building (76593)	87.1	8.8	3.6	0.5
	Vegetation (8943)	10.2	88.9	0.7	0.2
	Art. ground (49048)	2.2	2.1	84.6	11.1
	Nat. ground (1043)	4.1	∼ 0	33.2	62.7

A slack variable is a nonnegative variable that turns an inequality into an equality constraint.


F Baque

R Raillon

K Vulliez

B Chassignole

G Corneloup

French developments for improving In Service Inspection of SFRs

Keywords: SFR, ASTRID, Inspection, Non-Destructive-Examination, Viewing, Robotics

In Service Inspection (ISI) is a major issue to be taken into account for future Sodium Fast Reactors safety, thus, a large R&D work is performed since 2010 in France for the future SFRs.

ISI requirements have been taken into account since the early pre-conceptual design phase (specific rules for design have been merged into RCC-MRx design rules until 2012), then consolidated through the basic design phase with more detailed specifications leading to increase the ISI tools ability for immersed sodium structures of SFRs, at about 200°C (shut down conditions). Inspection within the main vessel are planned with transducers immersed in sodium and also with transducers located out of sodium medium.

Finally, the qualification of ISI ultrasonic transducers (for Non Destructive Examination, Telemetry and Imaging) is being performed with experimental water and sodium testing, to be compared to simulation with CIVA software platform results. A pluri-annual R&D program mainly deals with the reactor block structures, the primary components and circuit, and the Power Conversion System. Specific developments have been performed for NDE of thick austenitic steel welds, NDE using guided Lamb waves, telemetry from the outside of reactor vessel, imaging of immersed structures and components within the large primary vessel (in a pool type reactor concept) and associated in sodium robotics (with in-sodium tightness).

Some results of testing and simulation are given for some ASTRID project applications.

Introduction

Within the framework of the future Generation IV reactors, Sodium-cooled Fast Reactor have been considered by France as the most mature concept in 2008 and an extensive R&D programme was launched in 2010 [START_REF] Baqué | Service Inspection and Repair of Sodium cooled ASTRID Prototype[END_REF] . In that frame, In-Service Inspection has been identified as a difficult task to be performed (as sodium coolant is opaque, hot and highly chemically reactive) based on experience feedback (French Phenix and Superphenix SFRs, as well as foreign power plants). Ultrasonic techniques have been extensively studied as they are well adapted to Non-Destructive Examination and telemetry measurement in this harsh environment [START_REF] Baqué | In Service Inspection and Repair developments for SFRs, Oral talk at the Training Workshop on Online Monitoring, Non-Destructive Examination and In-Service Inspection of Research Reactors[END_REF] .

Improving the inspection tool deals with developing ultrasonic transducers to be immersed in sodium at about 200°C in the reactor block (ISI operations ar e performed at shutdown conditions). This is being done based on consolidated specifications and a pre-qualification process involving increasingly more realistic experiments using acoustic techniques and simulations performed with the patented CIVA code. ISI items (inspection: ultrasonic sensors, telemetry, vision and volumetric control, associated robotics) are being developed and qualified for the inspection of reactor block systems, structures and components, and for the power conversion system [START_REF] Baqué | In Service Inspection and Repair developments for SFRs. oral talk at the Symposium GIF[END_REF] . This program is ensuring the strong ties needed between the reactor designers and inspection specialists since the aim is to optimize inspectability. This already induced specific rules for design in order to shorten and facilitate the ISI operations: new rules have been merged into the RCC-MRx design code (2012 edition).

R&D effort depends on what is required for each component and structure, using the risk-informed method, which take into account potential damage and the consequences in case of a break of the considered structure or component.

Ultrasonic transducers for in-sodium inspection

Several ultrasonic transducers have been developed and tested [START_REF] Baqué | R&D status on insodium ultrasonic transducers for ASTRID inspection[END_REF] : among them, the TUSHT model by the CEA, the TUCSS model by FRAMATOME/INTERCONTROLE (both using piezoelectric materials), and electromagnetic acoustic transducers (EMAT) by the CEA. Each type of transducer has several advantages and disadvantages regarding the ultrasonic applications considered for ASTRID: transducer location, ultrasonic techniques ("contact" or "immersion", bulk or guide waves, etc.), temperature, and the type of measurement (telemetry/vision or defect detection). Their development and qualification programmes are currently underway which aim at ultimately selecting the most appropriate transducer for each ASTRID ultrasonic application. This involves tests on both simple and elaborate targets (with defects to be detected or specific shapes to be measured/seen), together with tests in water at room temperature and in sodium at 200°C. Simulation tool s have also been improved by developing the CIVA software platform for volumetric and guided wave techniques 6 .

In sodium telemetry: ultrasound wave propagation in a heterogeneous fluid medium

Inside a sodium cooling system, the environment is heterogeneous because of the complex flow states, particularly during operation. Even if they are lower in shutdown conditions, the effects of this heterogeneous environment cannot be disregarded with respect to acoustic propagation. It is thus necessary to carry out experiments with the objective of investigating developments for component technologies, though such experiments using liquid sodium tend to be relatively large-scale. This is why numerical simulation methods are essential prior to carrying out real experiments or filling up the limited number of experimental results. Though various numerical methods have been applied to wave propagation in liquid sodium, there is still no method for verifying heterogeneities on a three-dimensional scale. Moreover, it has also proved difficult to simulate such issues with conventional methods because a reactor core represents a complex acousto-elastic region. Thus, a study was launched to solve these two issues by applying a three-dimensional spectral element method [START_REF] Nagaso | Wave propagation simulation . Int. Conf. ANIMMA 2017[END_REF] . Initial results of a three-dimensional simulation study on heterogeneous media (the first point) was discussed. In order to take into account the heterogeneity of liquid sodium, four-dimensional temperature fields were used (three spatial and one temporal dimension) which were calculated by computational fluid dynamics (CFD) with large eddy simulation (LES), instead of using a conventional method (i.e. Gaussian Random field). This three-dimensional numerical experiment demonstrated that it is possible to check the effects of heterogeneous propagation media on waves in liquid sodium.

Non Destructive Examination using guided waves

Ultrasonic guided wave techniques are considered as suitable candidates for the inspection of welded structures within SFRs, as the long-range propagation of guided waves without amplitude attenuation can overcome the accessibility problem due to the liquid sodium.

Guided wave technique for defects detection within core supporting shell

Guided wave NDT methods can be applied to control the integrity of welds located in a junction-type structure welded to the main vessel. The method is based on the analysis of scattering matrices related to each expected defect, and takes advantage of the multi-modal and dispersive characteristics of guided wave generation. Simulations studies have shown the effectiveness of the method on a simplified junction-type structure immersed in air, but experiments were limited to the performance verification of the mode selection procedure on a structure without any defect.

Figure **: Inspection configuration (left) and modal identification (right)

The validity of this NDT method to characterize calibrated defects embedded in a junction-type structure has been investigated in detail [START_REF] Elie | Study of a guided wave technique for the characterization of defects within a complex junction in sodium cooled reactor[END_REF] . The detection capabilities of the method was evaluated through simulations and experimental campaigns, with the junction immersed in air then in water. In order to limit the displacement of the probes, time-frequency analysis using reassigned spectrograms was used to identify the modal content of guided wave signals from a single measurement point. The results show good agreement between experience and simulation as well as the detection of several defects in the junction. Finally, a simulation study of the modal propagation within a structure immersed in liquid sodium was conducted, in order to evaluate the attenuation caused by the fluid..

Study of Leaky Lamb Waves for Non-Destructive Testing of Structures behind Screen

The purpose is to evaluate the possibility of propagating Lamb waves to the inside of the reactor vessel while generating them from the outside, and to study their Non Destructive Testing (NDT) capability to detect and locate potential defects within immersed structures. The propagation of Lamb wave in a immersed plate (called Leaky Lamb Wave, or LLW) is first characterized and measured, with a focus on the attenuation due to the re-emission of energy into the surrounding fluid [START_REF] Kauffmann | Non-Destructive Testing of Nuclear Structures behind Screen using Leaky Lamb Waves[END_REF] .

The 2D Fourier transform and a time-frequency processing are used to measure velocity and attenuation while taking into account the dispersive and multimodal nature of propagation. The re-emitted acoustic field during multimodal propagation is analysed in detail, and the interferences occuring are explained. Then the propagation in a second plate is modelled by taking into account the energy supply re-emitted along the first plate. Due to this supply, the apparent attenuation in the second plate differs from the attenuation in the first plate and becomes lower. This behaviour is predicted and measured. Bulk wave paths in the fluid between plates are analyzed, and the interference they can cause on the Lamb waves in the plates is theorized and experimentally observed. Finally, the Energy Based Model (EBM) is successfully used to predict the return echo from a plane defect located in the second and third plates.

Non Destructive Examination of thick austenitic welds

Ultrasonic testing of austenitic stainless-steel multi-pass welds is very challenging. Because of the welded structure, both anisotropic and heterogeneous, the propagation of the ultrasonic beam is disrupted (attenuation, deviation, division), making diagnosis difficult. This diagnosis can be improved by using modelling codes in order to simulate the beam propagation and the beam to defect interaction in a weld [START_REF] Chassignole | Application of modelling for enhanced ultrasonic inspection of stainless steel welds[END_REF] . For this purpose, a realistic description of the microstructure is required [START_REF] Chassignole | Ultrasonic and structural characterization of anisotropic austenitic stainless steel welds: Towards a higher reliability in ultrasonic non-destructive testing[END_REF] . This can be obtained by macrography, but this involves a destructive cutting of the weld, or a weld sample. In the latter case, the sample must have been made and stored, and it must be representative of the weld. This weld description is then coupled to the ATHENA ultrasonic simulation code, developed by EDF R&D, to simulate the impact of welding on propagation and therefore control.

As an alternative, since 2000, EDF French Utilities and the Acoustics and Mechanics Lab (LMA) have been studying a numerical model (called MINA) for predicting the microstructure of SMAW multi-pass welds, which only takes into account information from welding conditions [START_REF] Moysan | Modelling the grain orientation of austenitic stainless steel multipass welds to improve ultrasonic assessment of structural integrity[END_REF] . The order of the passes, in particular, and its consequences on the solidification of the molten bath, have shown that the growth of the grains has a predictable direction. The MINA model developed estimates this texture. The application to SFR components of this methodology developed for PWR welds is discussed in this section.

The testing configuration is presented on next figure. The welds to be inspected connect two horizontal and one vertical plates made in 316L(N) steel and with a 40 mm thickness. This study focuses on the detection of two plane defect (notch type), referenced E3 and E4.The grain orientation mapping in the weld was determined on the weld macrography given on Figure 1b. The elastic properties and attenuation coefficients have been drawn from previous studies on 316L austenitic welds [START_REF] Ploix | Measurement of ultrasonic scattering attenuation in austenitic stainless steel welds: Realistic input data for NDT numerical modeling[END_REF] . Detection of the E4 notch requires 45° wave propaga tion and multiple reflections on the vertical plate. Shear waves are given first consideration as they do not lead to mode conservation echoes in this particular configuration. However, an experimental result concludes to the failure in detecting the E4 notch with this propagation mode. This result was explained by ATHENA calculation. Indeed, the weld microstructure is too disrupting for shear waves (significant beam deviation and division) to detect this in-depth defect. The beam disruption is illustrated on next figure). In conclusion, those examples well illustrate the usefulness of numerical approach to explain the propation phenomena in complex polycristalline materials, and also to help in the design to an ultrasonic technic by taking into account the set of the influential parameters (material, geometry,…).

In the same way, a collaborative work between LMA and CEA, develop a similar model to MINA for GTAW [START_REF] Marsac | MINA-TIG: a model for predicting the microstructure of a multi-pass GTAW weld (TIG) of austenitic stainless steel[END_REF] (or TIG, so MINA TIG). The consideration of this specific metallurgical aspects has led to the introduction of new parameters. So, in contrast of SMAW where fluid flows (Marangoni, Lorentz, …) in the weld pool are constant or negligible, whereas, in case of GTAW welding process, these effects are not negligible and not constant. They depend strongly of welding conditions (voltage, current, speed, electrode tip angle, composition and flow of shielding gas) and also the chemical composition of steel and shielding gas (surface active elements).

Near distance under-sodium viewing

The aim is to develop and qualify inspection tools for viewing of sodium-immersed structures and components, imaging of small defects (e.g. open flaws in a weld) and looking for lost parts (if any), at short distance (less than 200mm). Sodium tests were performed at 200°C in the DOLMEN sodium CEA circuit, with the VENUS robot, which can move sensors with a high level of accuracy, despite the environmental constraints due to the sodium medium and high temperature level [START_REF] Gobillot | Under-Sodium Imaging of SFR Internals -Four degrees of freedom with a sodium transducer displacement system[END_REF] .

Images were reconstructed and demonstrate the operational visualization capacity (for near distance imaging i.e. less than 200mm distance) of such techniques through the reconstruction of 3D images obtained from the ultrasonic tests performed in static sodium at 200°C. As the distance between acoust ic sensor and target to be imaged is rather small, a specific robotic system (so called VENUS robot) was designed, manufactured and used in 200°C static bul k sodium. VENUS instrumentation & control (I&C) system was combined with two high-temperature ultrasonic transducers (TUSHT) and a suitable processing method for the signals. These sensors are used to emit ultrasounds and receive the resulting echoes reflected by targets immersed in liquid sodium. The high-temperature ultrasonic transducers (TUSHT) used can be described as single-element piezoelectric sensors, with one being flat and the other being equipped with a focusing lens.

Long distance under-sodium viewing

Sub-marine techniques

The aim is to develop and qualify inspection tools for viewing of global scenery sodium-immersed structures and components (with possible displacements and deformations), imaging of specific targets and looking for lost parts (if any), at long distance (from 200mm to some meters).

This work was partially performed with the Marine Physical Laboratory (san Diego USA) in order to take advantage of available submarine acoustic processes [START_REF] Lubeigt | Passive structural monitoring based on matched processing[END_REF] : indeed, passive structural monitoring can be performed using Matched Field Processing technique. The source localization method is based on the comparison between measured data D , and forward field G , | derived by a propagation model at a given frequency.

Figure *: Match Field Process principle (left). Measured and forward acoustic fields. Subtracting operation between reference and damage states (right)

Taking into account the main conditions of SFR operation, some submarine acoustic techniques based on acoustic source localization, such as Matched Field Processing, seem likely to detect and localize local defects in a given structure: it should be possible to detect abnormal structure and component evolution within the primary sodium loop, using the defect's acoustical signature. As one has no propagation model, but do have sensors all around the structure, a model can be built based on measured and forward acoustic fields data. Furthermore, using lower frequency range, a passive approach could use the ambient noise (within the main vessel) for a global imaging.

For this, Green functions reconstruction have been performed with ambient noise data measurement. Then, signal treatment for defect localization is undertaken with numerical data for a twodimension example. When an acoustic wave hits the defect, it acts as a point source. The residuals computed at the receivers isolate the defect's signature. The subtractive operation removes the contribution of the sources. When a wave hits the defect, it acts as a point source. By a subtractive operation at the receivers between data from the reference and damaged structures, one can isolate the defect's signature.

Conventional Matched Field Processing has been used for the case of a local defect in a cylindrical structure, using 13 acoustic frequency ranges from 7 kHz to 10 kHz. Using incoherent ambient noise, the defect is detected and located by a 5 dB peak above highest side-lobes.

Figure *: Detection of a local defect with Matched Field Processing

Next step is an experimental program for the qualification phase, with water-immersed mockup at small scale.

Phased-array sensor for long distance under-sodium viewing

An ultrasound imaging system made up of 2 orthogonal antennas was developed for long distance vision in liquid sodium [START_REF] Imbert | Fast Under-Sodium Viewing. Application to Sodium Fast Reactors[END_REF] . Recently, a simulation study was performed with CIVA that allow understanding the phenomena involved in the antenna radiated beamforming and echo response, as well as the effect of important parameters on its imaging capabilities. This study allows to determine the suitable values of the antennas parameters for the targeted practical applications At the same time, 2 antennas with 128 elements each, built in 1999 at CEA Cadarache, were used for under water experimental trials. The results obtained for different targets and different types of acquisitions (mechanical C-scans, Full Matrix Capture and Total Focusing Method reconstruction) allowed to confirm the good performances of the system in terms of sensitivity and spatial resolution and to validate the predictions of CIVA.

In-sodium Robotics Technological bricks for Under Sodium Robotics

The maintenance of SFRs (inspection, repair) will be performed during shut down periods with some robotic carriers which have to be introduced within the main vessel, in primary 200°C sodium coolant with argon gas coverture. Inspection campaigns should be less than one month.

These robots (or carriers) will allow bringing inspection and repairing tools up to concerned components and structures. The needed degrees of freedom associated to these operations will be assumed either directly by the carrier itself or by specifics lower end carrier device for accurate local positioning. Several carriers will be designed, well adapted to specific needs: type of maintenance operation and location of inspection and repair sites.

Feedback experience was gained during Superphenix SFR operation with the MIR robot which allowed to successfully make the Non Destructive Examination of main vessel welding joints, the carrier being outside bulk sodium. Operating conditions for SFR robots will be harder from those of the MIR robot: about 200°C temperature, radiation dose ranging from 105 to 106 Gy, and especially mainly direct immersion within liquid sodium coolant.

At the design phase of these robots, three main configurations have been considered, depending on the adopted solution for robot component seclusion: zone 1 with tight surrounding shell cooled by argon gas flow (constraints: irradiation and 70°C t emperature), zone 2 with not cooled tight surrounding shell (constraints: irradiation and 180°C-200°C temperature), zone 3 with no tight surrounding shell (constraints: irradiation, 180°C-200°C temper ature and immersion within liquid sodium).

It appears that some technical solutions do exist for future in sodium carriers, using available trade components. Nevertheless, qualification tests will be necessary in order to confirm some specific components (such as polymers, greases, sensors, reducers, motors, bearings). For bearings, specific test program are performed in order to check their capability for static and dynamic loadings during in sodium operation (without lubricant). Electrical motor development for 200°C operation was performed and first under sodium tests are on the way in 2019 [START_REF] Jouan | Generation IV SFR Nuclear Reactors: under Sodium Robotics for ASTRID[END_REF] .

Sealing issues for the SFRs

A dedicated R&D program is conducted to design and qualify a sodium-tight diving bell to board the inspection and tools [START_REF] Vulliez | CEA R&D program on sealing issues for the SFRs[END_REF] . This bell will be attached to a Remote Handling arm and will be moved inside the nuclear vessel then docked on its inners surface. The docking of the bell compresses a seal against the surface to explore/repair, once the inner volume of the seal drained a shutter opens a window to allow the deployment of the ISI tools. The development of the system focuses on the conception of a reliable mechanical system (a rocking shutter that can be opened and closed by two double-acting pneumatic actuators) and on sealing related issues.

If the mechanical part was challenging, the development of elastomer based seals compatible with a use under high temperature liquid sodium remains the most critical point. Safe for some reserve concerning the drop of the sealing performance of the seal of the docking flange due to the chemical degradation of the elastomer with the liquid sodium, the system successfully passed through series of qualification tests. More details regarding the R&D program can be found in this conference.

Conclusions and perspectives

For the future SFR Generation IV reactors, France launched an extensive R&D programme in 2010 [START_REF] Baqué | Service Inspection and Repair of Sodium cooled ASTRID Prototype[END_REF] . In-Service Inspection has been identified as a key point and ultrasonic techniques have been extensively studied for Non-Destructive Examination and telemetry/imaging.

After specifying the links between SFR design, structure materials, access for inspection, inspection techniques and tools, accurate connection between ultrasonic propagation and stainless steel material microstructure is looked at.

Dedicated ultrasonic transducers, well adapted to the specific Non Destructive Examination applications (flaw detection and sizing, imaging…), are developed for each application, in connexion with simulation made with CIVA software platform. A three-dimensional numerical experiment demonstrated that it is possible to check the effects of heterogeneous propagation media on acoustic waves in liquid sodium.

Guided wave NDT methods are applied to control the integrity of welds: time-frequency analysis using reassigned spectrograms is used to identify the modal content of guided wave signals from a single measurement point. It was also demonstrated that it is possible to propagate Lamb waves to the inside of the reactor vessel while generating them from the outside, and to study their Non Destructive Testing capability to detect and locate potential defects within immersed structures.

Near distance under sodium imaging (less than 20cm) was performed using a robot with four degrees of freedom: 3D images, based on the ultrasonic echoes of TUSHT sensor with a focusing lens, were reconstructed with 500 µm wide grooves, letters and barre-coding.

Longer distance under sodium imaging (more than 20cm) is studied using Matched Field Processing and associated techniques from submarine area, for global viewing and large defect detection. An ultrasound imaging system made up of 2 orthogonal antennas is developed: a first prototype will be available by end 2019 for qualification under sodium tests.

Robotic systems are developed, based on rotation and elevation movements. Qualification tests are performed for various components (polymers, greases, sensors, reducers, motors, bearings). Electrical motor is available for 200°C under sodium operation .

A dedicated sodium-tight diving bell has been designed and is being qualified for ISI tools. The development of elastomer based seals compatible with a use under high temperature liquid sodium remains the most critical point.

  As a perspective, a new PhD work was launched by end 2018 in order to study the contribution of time reversal technique for ultrasounds nondestructive testing for generation and propagation of Lamb waves within multi-plate structures.

ICAPP 2019 -

 2019 Figure **: Inspection configuration (left) and under water results with two 8mm thick plates (right)

Figure

  Figure **: Inspection configurations (left) and weld microstructure (right)

Figure * * :

 * Figure **: Examples of beam visualization in the weld from ATHENA2D calculation for L0 wave (left) and SW45 (right)

Figure * :

 * Figure *: TUSHT sensor located on the VENUS device (view before sodium tests)By processing the resulting signals, the data can be used to reconstruct images that indicate the position, size and shape of the different parts of the targets. The VISION mock-up was used to visualize and detect defects at the liquid-solid interface: it was equipped with 4 grooves and series of letters ("CEA") engraved on the plate, as well as a triangular inset with sharp edges and a pipe with an elbow.

Figure * :

 * Figure *: Ultrasound images of the VISION mock-up: triangle, pipe, grooves and letters, with flat front face TUSHT Ø40 mm coupled with focusing lens (at 6.5 ± 0.4 MHz)The scanning was carried out using the VENUS robot with four degrees of freedom: this made it possible to demonstrate the ability for close visualization (between 150 and 200mm distance) of objects in sodium under operational conditions. This was done by reconstructing a 3D image based on the ultrasonic echoes, as well as by providing a representation of the grooves simulating open cracks and barrecoding. The grooves, including the thinnest one which was 500 µm wide, were detected by the TUSHT Ø 40 with a focusing lens.

Figure *: 2

 2 Figure *: 2 antenna concept and under water imaging of a 30mm nut at 500mm distance

Figure * :

 * Figure *: Under sodium rotation mockup (left) and calculated motor temperature map (right)

Acknowledgment

All the presented results have been conducted and funded by Sodium Fast Reactor Project within Generation 4 frame at CEA and EDF. The authors would like to thank their colleagues who take part to this strong effort for improving inspection in this harsh conditions.