D Tuia

N Courty

R Flamary
email: remi.flamary@unice.fr

A GROUP-LASSO ACTIVE SET STRATEGY FOR MULTICLASS HYPERSPECTRAL IMAGE CLASSIFICATION

Keywords: Algorithms, Learning, Feature extraction, Classification, High resolution, Hyper spectral

Hyperspectral images have a strong potential for landcover/landuse classification, since the spectra of the pixels can highlight subtle differences between materials and provide information beyond the visible spectrum. Yet, a limitation of most current approaches is the hypothesis of spatial independence between samples: images are spatially correlated and the classification map should exhibit spatial regularity. One way of integrating spatial smoothness is to augment the input spectral space with filtered versions of the bands. However, open questions remain, such as the selection of the bands to be filtered, or the filterbank to be used. In this paper, we consider the entirety of the possible spatial filters by using an incremental feature learning strategy that assesses whether a candidate feature would improve the model if added to the current input space. Our approach is based on a multiclass logistic classifier with group-lasso regularization. The optimization of this classifier yields an optimality condition, that can easily be used to assess the interest of a candidate feature without retraining the model, thus allowing drastic savings in computational time. We apply the proposed method to three challenging hyperspectral classification scenarios, including agricultural and urban data, and study both the ability of the incremental setting to learn features that always improve the model and the nature of the features selected.

INTRODUCTION

Remote sensing technologies allow to observe the Earth from a distance. The use of satellite and aerial data allows to monitor the processes occurring at the surface in a non-extrusive way, both at the local and global scale [Lillesand et al., 2008, Richards and[START_REF] Richards | Remote Sensing Digital Image Analysis: An Introduction[END_REF]. The reduced revisit time of satellites, in conjunction with the potential for quick deployment of aerial and unmanned systems, made remote sensing systems more and more appealing and nowadays the use of satellite data has become a standard for researchers and public bodies.

In order to be usable by end-users and decision makers, remote sensing pixel information must be processed and converted into products depicting a particular facet of the processes occurring at the surface. Among the different products traditionally available, land cover maps issued from image classification1 are the most common (and probably also the most used). Land cover maps can then be used for urban planning [Taubenbock et al., 2012], agriculture surveys [START_REF] Alcantara | Mapping abandoned agriculture with multi-temporal MODIS satellite data[END_REF] or surveying of deforestation [START_REF] Asner | Ecology: Selective logging in the Brazilian Amazon[END_REF].

The quality of land cover maps is of prime importance. Therefore, a wide panel of research works consider image classification algorithms and their impact on the final maps [START_REF] Plaza | Recent advances in techniques for hyperspectral image processing[END_REF][START_REF] Camps-Valls | Remote Sensing Image Processing[END_REF]. This challenge is not trivial, as remote sensing systems are often high dimensional ( number of spectral bands acquired), spatially and spectrally correlated and affected by noise [START_REF] Camps-Valls | Advances in hyperspectral image classification[END_REF]. Moreover, temporal dependencies are also present, since a type of land cover evolves throughout the year.

Among these aspects of the data, spatial relations have received particular attention [START_REF] Fauvel | Advances in spectral-spatial classification of hyperspectral images[END_REF]: the land cover maps are generally smooth, in the sense that neighboring pixels tend to belong to the same type of land cover [START_REF] Schindler | An overview and comparison of smooth labeling methods for land-cover classification[END_REF]. On the contrary, the spectral signatures of pixels of a same type of cover tend to become more and more variable, especially with the increase of spatial resolution. Therefore, we want to describe a smooth land cover random field using spectral information with a high within-class variability. Solutions to this problem have been proposed in the community and mostly recur to spatial filtering [START_REF] Fauvel | Advances in spectral-spatial classification of hyperspectral images[END_REF] -i.e., work at the level of the input vector -and Random Fields/graph cuts [START_REF] Moser | Landcover mapping by Markov modeling of spatial-contextual information[END_REF] -i.e. work within the optimization of a context-aware energy function.

In this paper, we consider the first family of methods, i.e. those based on the extraction of spatial filters. Methods proposed in remote sensing image classification tend to pre-compute a large quantity of spatial filters, related to the user's preference and knowledge of the problem: texture [START_REF] Pacifici | A neural network approach using multi-scale textural metrics from very highresolution panchromatic imagery for urban land-use classification[END_REF], Gabor [Li and Du, in press], morphological [START_REF] Benediktsson | Classification of hyperspectral data from urban areas based on extended morphological profiles[END_REF][START_REF] Tuia | Classification of very high spatial resolution imagery using mathematical morphology and support vector machines[END_REF][START_REF] Dalla Mura | Morphological attribute profiles for the analysis of very high resolution images[END_REF] or bilateral filters [START_REF] Schindler | An overview and comparison of smooth labeling methods for land-cover classification[END_REF] are among those used in recent literature.

Even if successful, these studies relied on the definition a-priori of a filterbank. As shown in Fig. 1a, the filter bank is applied to each band of the image, resulting into a (f ×b)-dimensional filter bank, where f is the number of filters and b the number of bands. This proved to be unfeasible for high dimensional datasets, such as hyperspectral data, for which the traditional way to deal with the problem is to perform a principal components analysis (PCA) and then extract the filters from the p << b principal components related to maximal variance [START_REF] Benediktsson | Classification of hyperspectral data from urban areas based on extended morphological profiles[END_REF]. In that case, the final input space is (f × p)-dimensional. In all cases, the dimensionality of the final vector makes it necessary to run a feature selection step, to select the subset which is the most effective for the classification. Proceeding this way is suboptimal in two senses: first, one forces to restrict the number and parameters of filters to be used to a subset, whose appropriateness only depends on the prior knowledge of the user. Second, generating hundreds (if not thousands) of spatial filters and use them in a classifier, that also might operate with a feature selection strategy, increases the computational cost significantly. Also note that, if the spatial filters considered bear continuous parameters (e.g. Gabor or angular features), there is theoretically an infinite number of feature candidates. An integrated approach, which would incrementally build the set of filters from an empty subset and select only the filters that improve class discrimination to the classifier is of great importance. Two approaches are of particular interest in this sense: Grafting [START_REF] Perkins | Grafting: Fast, incremental feature selection by gradient descent in function space[END_REF] and Group Feature Learning [START_REF] Rakotomamonjy | Learning with infinitely many features[END_REF], which incrementally select the most promising feature among a batch of features extracted from the universe of all possible features admitted. Since this selection is based on a heuristic criterion ranking the features by their informativeness when added to the model, it may be seen as performing active learning [START_REF] Crawford | Active learning: Any value for classification of remotely sensed data?[END_REF] in the space of possible features (in this case, the active learning oracle is replaced by the optimality condition, for which only features improving the current classifier are selected).

In this paper, we extend the Group Feature Learning model [START_REF] Rakotomamonjy | Learning with infinitely many features[END_REF] to multiclass logistic regression (also known as multinomial regression). The use of a group-lasso regularization [START_REF] Yuan | Model selection and estimation in regression with grouped variables[END_REF] allows to jointly select the relevant features and also to derive efficient conditions for evaluating the discriminative power of a new feature. In [START_REF] Rakotomamonjy | Learning with infinitely many features[END_REF], authors propose to use group-lasso for multitask learning by allowing to use an additional sparse average classifier common to all tasks. Adapting their model in a multiclass classification setting leads to the use of the sole group-lasso regularization. Note that one could use a ℓ1 support vector machine as in [Tuia et al., in press] to select the relevant feature in a One-VS-All setting but this approach is particularly computationally intensive, as the incremental problem is solved for each class separately. This implies the generation of millions of features, that may be useful for more than one class at a time. This approach indeed allows to natively handle several classes without using the well known One-Against-All approach and has the advantage of providing probabilistic prediction scores that can more easily be used in any post-processing methods (markovrandom fields,. . . ).

We test the proposed method on two landcover classification tasks with hyperspectral images of agricultural areas and on one landuse classification example over an urban area exploiting jointly hyperspectral and LiDAR images. In all cases, the proposed feature learning method solves the classification tasks with at least state of the art numerical performances and returns compact models including only features that are discriminative for more than one class.

The remainder of this paper is as follows: Section 2. details the proposed method, as well as the multiclass feature selection using group-Lasso. In Section 3., we present the datasets and the experimental setup. In Section 4. we present and discuss the experimental results. Section 5. concludes the paper.

FEATURE LEARNING WITH MULTICLASS LOGISTIC CLASSIFICATION

The proposed methodology is described in this Section. We first present the multiclass logistic classification and then derive its optimality conditions, which are used in the active set algorithm.

Multiclass logistic classifier with group Lasso regularization

Consider an image composed of pixels xi ∈ R d . A subset of lc pixels is labeled into one of C classes: {xi, yi} lc i=1 , where yi are integer values ∈ {1, . . . , C}. We consider a (possibly infinite) set of θ-parametrized functions φ θ (•) mapping each pixel in the image into the feature space of the filter defined by θ. As in [Tuia et al., in press], we define as F the set of all possible finite subsets of features and ϕ as an element of F composed of d features ϕ = {φ θ j } d j=1 . We also define Φϕ(xi) as the stacked vector of all the values obained by applying the filters ϕ to pixel xi and Φϕ ∈ R n l ×d the matrix containing the features in ϕ computed for all the labeled pixels.

In this paper we consider the classification problem as a multiclass logistic regression problem. Learning such a classifier for a fixed amount of features ϕ corresponds to learning a weight matrix W ∈ R d×c and the bias vector b ∈ R 1×C using the softmax loss. In the following, we refer to wc as the weights corresponding to class c, which corresponds to the c-th column of matrix W.

The k-th line of matrix W is denoted as W k,• . The optimization problem for a fixed feature set ϕ is defined as:

L(ϕ) = min W,b 1 lc lc i=1 log C c=1 exp (wc -wy i ) ⊤ Φϕ(xi) + (bc -by i ) + λΩ(W) (1)
where the first term corresponds to the soft-max loss and the second term is a group lasso regularizer. In this paper, we use the mixed ℓ1 -ℓ2 norm:

Ω(W) = d k=1 ||W k,• ||2 (2)
This regularization term promotes group sparsity, due to its non differentiability at zero. In this case we grouped the coefficients of W by lines, meaning that it will promote joint feature selection for all classes. Note that this approach can be seen as multi-task learning where the tasks corresponds to the classifier weights of each class [START_REF] Obozinski | Multi-task feature selection[END_REF][START_REF] Rakotomamonjy | lp-lq penalty for sparse linear and sparse multiple kernel multitask learning[END_REF]. As a result, if a variable (filter) is active, it will be active for all classes. In our opinion, this makes particular sense in a multiclass setting, because a feature that helps in detecting a given class also helps in "not detecting" the others in the C -1 other classifiers.

In order to discuss the proposed algorithm, we first have to derive the optimality conditions of the problem. To this end, we compute the sub-differential of the cost function defined in Eq. ( 1):

∂L = Φ ⊤ ϕ R + λ∂Ω(W) (3)
where, R is a lc × C matrix that, for a given sample i ∈ {1, ., lc} and a class c ∈ {1, ., C}, equals:

Ri,c = exp(Mi,c -Mi,y i ) -δ {y i -c} C k=1 exp(M i,k -Mi,y i ) lc C k=1 exp(M i,k -Mi,y i ) (4
) where M = ΦϕW + 1b and δ {y i -c} = 1 if c = yi and 0 otherwise. In the following, we set G = Φ ⊤ ϕ R a d × C matrix corresponding to the gradient of the data fitting term w.r.t W. Note that this gradient is simply computed by multiple scalar product between the features Φϕ and the multiclass residual R. The optimality conditions can be obtained separately for each group j, i.e. for each line j of the W matrix. Ω(W) is a non differentiable norm-based regularization [START_REF] Bach | Convex optimization with sparsity-inducing norms[END_REF]. The optimality condition for an Euclidean norm regularization consists in a constraint with its dual norm (namely itself):

||Gj,•||2 ≤ λ ∀j ∈ ϕ (5)
which in turn breaks down to: Compute G as in ( 7) ∀j = 1 . . . p

||Gj,•||2 = λ if Wj,• = 0 ||Gj,•||2 ≤ λ if Wj,• = 0 (6)

5:

Find feature φ * θ j maximizing ||r θ j ||2

6:

if ||G θ * j ,• ||2 > λ + ǫ then 7: ϕ = φ * θ j ∪ ϕ 8:
end if 9: until stopping criterion is met residual matrix is below λ, it means that this feature is not useful and its weight will be set to 0 for all the classes.

Proposed active set criterion

In this paper, we want to learn jointly the best set of filters ϕ * ∈ F and the corresponding MLC classifier. This is achieved by minimizing Eq. ( 1) jointly on ϕ and W, b, respectively. As in [START_REF] Rakotomamonjy | Learning with infinitely many features[END_REF], we can extend the optimality conditions in (6) to all filters with zero weights that are not included in the current active set ϕ:

||G φ θ ,• ||2 ≤ λ ∀φ θ / ∈ ϕ (7)
Indeed, if this constraint holds for a given feature not in the current active set, then adding this feature to the optimization problem will lead to a row of zero weights W (d+1),• for this feature. This also means that if we find a feature that violates Eq. ( 7), its inclusion in ϕ will i) make the global MLC cost decrease and ii) provide a feature with non-zero coefficients for all classes after reoptimization.

The proposed algorithm is illustrated in Fig. 1b and pseudocode is given in Algorithm 1: we initialize the active set ϕ0 with the spectral bands and run a first MLC minimizing Eq. ( 1). Then we generate a random minibatch of candidate features, Φ θ j , involving spatial filters with random types and parameters. We then assess the optimality conditions with (7): if the feature φ * θ j with maximal ||G θ j ,• ||2 is greater than λ + ǫ, it is selected and added to the current active set [φ * θ j ∪ ϕ].

DATA AND SETUP

Datasets

We tested the proposed active set method on three hyperspectral classification tasks: a) Indian Pines 1992 (AVIRIS spectrometer, HS): the first dataset is a 20-m resolution image taken over the Indian Pines (IN) test site in June 1992 (see Fig. 2). The image is 145 × 145 pixels and contains 220 spectral bands. A ground survey of 10366 pixels, distributed in 16 crop types classes, is available. The classes are unevenly sampled (see Table 1). This dataset is a classical benchmark to validate model accuracy and is known to be very challenging because of the strong mixture of the classes' signatures, since the image has been acquired shortly after the crops were planted. As a consequence, all signatures are contaminated by soil signature, making thus a spectral-spatial processing compulsory to solve the classification problem. As preprocessing, 20 noisy bands covering the region of water absorption have been removed. The third dataset depicts an urban area nearby the campus of the University of Houston (see Fig. 4). The dataset was proposed as the challenge of the IEEE IADF Data Fusion Contest 2013 [START_REF] Pacifici | Report on the 2013 IEEE GRSS data fusion contest: Fusion of hyperspectral and LiDAR data[END_REF]. The area has been imaged by the CASI hyperspectral sensor (144 spectral bands at 2.5m resolution) and scanned by a LiDAR. From the latter, a digital surface model (DSM) at the same resolution has been extracted. Both imaging sources have been coregistered. 15 urban land-use classes are to be classified (Tab. 3). Two preprocessing steps have been performed: 1) histogram matching has been applied to the large shadowed area in the right part of the image (cf. Fig 4 : the shadowed area has been extracted by segmenting a near-infrared band and the matching with the rest of the image has been applied. 2) A height trend has been removed from the DSM, by applying a linear detrending of 3m from the West along the x-axis.

Setup of experiments

For all dataset, all the features have been mean-centered and normalized to unit norm. This normalization is mandatory due to the optimality conditions, which is based on a scalar product. In order to compare fairly the alignment of all the candidate features to the residual, all feature must have the same norm (See Eq. ( 7).).

In all experiments, we use the multiclass logistic classifier with ℓ1 -ℓ2 norm implemented in the SPAMS package2 . We start by training a model with all available bands (plus the DSM in the HOUSTON2013 case) and use its result as the first active set.

Regarding the active set, we used the following parameters:

-The stopping criterion is a number of iterations (150).

-A minibatch is composed of filters extracted from 30 bands, randomly selected. In the HOUSTON 2013 case, the DSM is added to each minibatch. -The possible filters are listed in Tab. 4. These filters are those generally used in remote sensing hyperspectral classification literature [START_REF] Fauvel | Advances in spectral-spatial classification of hyperspectral images[END_REF], but any type of spatial filter can be used in the process. Structuring elements (SE) can be disks, diamonds, squares or lines. If a linear structuring elements is selected, an additional orientation parameter is also generated (α ∈ [-π/2, . . . π/2]).

-A single minibatch can be used twice (i.e. once a first filter has been selected, it is removed and Eq. ( 7) is re-evaluated on the remaining filters).

In each experiment, we start by selecting an equal number of labeled pixels per class lc: we extracted 30 random pixels per class in the INDIAN PINES 1992 case, 60 in the INDIAN PINES 2010 and in the HOUSTON 2013 case. The difference in the amount of labeled pixels per class is related to i) the amount of labeled pixels available per task and ii) the complexity of the problem at hand. As test set, we considered all remaining labeled pixels, but disregard those in the spatial vicinity of the pixels used for training. In the INDIAN PINES 1992 case, we consider all labeled pixels out of a 3 × 3 window around the training pixels, in the INDIAN PINES 2010 case a 7 × 7 window. The difference is basically related to the image resolutions. In the HOUSTON 2013 case, a spatially disjoint test set was provided in a separate file and was used testing purposes.

Each experiment was repeated 10 times, by varying the initial training set (the test set also varies, since it depends on the specific location of the training samples). Average performances, along with their standard deviation, are reported.

EXPERIMENTAL RESULTS

Performances along the iterations. Numerical results for the three datasets are provided in Fig. 5: the left column illustrates the evolution of the Kappa statistic [?] along the iterations and for three levels of ℓ1 -ℓ2 regularization λ. The right column shows the evolution of the number of features in the active set.

For all the datasets, the iterative feature learning corresponds to a continuous, almost monotonic, increase of the performance. This is related to the optimality conditions of Eq. ( 1 model adds one filter φ θ * j to ϕ, the MLC cost function decreases while the classifier performances raises. Overfitting is prevented by the group-lasso regularization: on the one hand this regularizer promotes sparsity through the ℓ1 norm, while on the other hand it limits the magnitude of the weight coefficients W and promotes smoothness of the decision function by the use of the ℓ2 norm. Note that for the HOUSTON 2013 dataset, the final classification performance is at the same level as the one of the winners of the contest, thus showing the credibility of our approach.

For each case study, the model with the lowest sparsity (λ = 0.0001) shows the initial best performance (it utilizes more features, as shown in the right column) and then keeps providing the best performances. However, the model with λ = 0.001 has an initial sparser solution and shows a steeper increase of the curve in the first iterations. When both models provide similar performance, they are actually using the same number of features in all cases. The sparsest model (λ = 0.01) shows the worst results in two out of the three datasets and in general is related to less features selected: our interpretation is that the regularization (λ = 0.01) is too strong, leading to a model that discards relevant features and is too biased for a good prediction (even when more features are added). As a consequence, the learning rate is surely steeper than for the other models, but the model does not converge to an optimal solution.

Numerical performances at the end of the feature learning. Comparisons with competing strategies where the MLC classifier is learned on pre-defined feature sets are reported in Table 5. First we discuss the performance of our active set approach when learning the filters applied on the original bands (AS-BANDS): in the INDIAN PINES 1992 case, the AS-BANDS method obtains an average Kappa of 0.83 using 96 features. This is a good result if compared to the upper bound of 0.86 obtained by a classifier We compared these results to those obtained by classifiers trained on fixed raw bands (MLC-ω) or on sets of morphological and attribute filters extracted form the three first principal components (MLC-s). We followed the generally admitted hypothesis that the first(s) PCA(s) contain most of the relevant information in hyperspectral images [START_REF] Benediktsson | Classification of hyperspectral data from urban areas based on extended morphological profiles[END_REF]. On all the datasets, the proposed AS-BANDS method performs remarkably well compared with models using only the spectral information (MLC-ω) and compares at worse equivalently (and significantly better in the INDIAN PINES 2010 and HOUSTON 2013 cases) with models using ℓ2 classifiers (thus without sparsity) and three to four times more features. The good performance of the ℓ2 method on the INDIAN PINES 1992 dataset (Kappa observed of 0.85) is probably due to the application of the PCA transform prior to classification, which, besides allowing to decrease the dimensionality of the data, also decorrelates the signals and isolates the bare soil reflectance, which is present for almost all classes (cf. the data description). For this reason, we also investigated a variant of our approach where, instead of working on the spectral space, we filtered the PCA components extracted from the original data (AS-PCAS). In the INDIAN PINES 1992 case, the increase in performance is striking, with a final Kappa of 0.89. For the two other datasets, the results remain in the same range as for the AS-BANDS results.

Selected features: for the three images, the active set models end up with a maximum of 70 -100 features, shared by all classes. This model is very compact, since it corresponds to only 30 -50% of the initial dimensionality of the spectra. Due to the optimization problem, the features selected are active for several classes simultaneously, as shown in Fig. 6, which illustrates the weights matrix W ⊤ for the INDIAN PINES 2010 andHOUSTON 2013 experiments at the end of the feature learning for one specific run with λ = 0.0001. Each column corresponds to a feature selected by the proposed algorithm and each row to a class; the color corresponds to the strength of the weight (positive or negative). One can appreciate that the selected features (columns) have large coefficients -corresponding to strong green or brown tones in the figures -for more than one class (the rows). By analysis of the features learned, one can appreciate that they clearly are discriminative for the specific classification problem: this shows that, by decreasing the overall loss, adding these features to the active set really improves class discrimination.

CONCLUDING REMARKS

In this paper we proposed an active set algorithm for the automatic selection of contextual features in hyperspectral image classification. The proposed method uses an optimality criterion based on the mixed-norm group lasso and the multiclass logistic regression classifier. For a minibatch of candidate features, it selects those that lead to a non-null coefficient in all the classes and therefore yields a decrease in the cost function. Compared to other existing active set algorithms, our approach integrates all the classes information simultaneously through the multiclass soft-max loss function, avoids the computation of irrelevant features with respect to the multi class classification problem and is much faster, since it solves a single active set problem instead of one per class. Experiments on three benchmark hyperspectral images illustrated the benefits of the approach, which reaches at least state of the art performances with a reduced set of features, and without the need of defining them by prior/expert knowledge. Extension to contextual classifier based on spatial priors (MRF, CRF) is the logical next step.

Figure 1 :

 1 Figure 1: Spatio-spectral classification with contextual filters. (a) Using pre-defined filterbanks, applied on the first principal component (p = 1). (b) The proposed incremental system based on active set and sparse SVM.

Figure 2 :

 2 Figure 2: Indian Pines 1992 AVIRIS data.(a) False color composition and (b) Ground truth (for color legend, see Tab. 1). Unlabeled samples are in black.

Figure 3 :

 3 Figure 3: Indian Pines 2010 SpecTIR data.(a) RGB composition and (b) Ground truth (for color legend, see Tab. 2). Unlabeled samples are in black.

Figure 4 :

 4 Figure 4: Houston 2013.(a) RGB composition of the CASI data, (b) DSM issued from the LiDAR point cloud and (c) ground truth. (for color legend, see Tab. 2). Unlabeled samples are in black.

Figure 5 :

 5 Figure 5: Left: numerical performance (Kappa statistic) for different degrees of regularization λ and filtering the original bands. Right: number of active features during the iterations.

Figure 6 :

 6 Figure 6: Final weight matrix for a run of the INDIAN PINES 2010 (top) and HOUSTON 2013 (bottom) experiments.Finally, Fig.7illustrates some of the features selected for the HOUSTON 2013 case. Each column corresponds to a different zoom in the area and highlights a specific class. We visualized the features of the same run as the bottom row of Fig.6and visualized the six features with highest ||Wj,•||2, corresponding to those active for most classes with the highest squared weights. By analysis of the features learned, one can appreciate that they clearly are discriminative for the specific classification problem: this shows that, by decreasing the overall loss, adding these features to the active set really improves class discrimination.

Table 1 :

 1 Classes and Samples (n c l ) of the ground truth of the Indian Pines 1992 dataset (cf. Fig.2).

	Class	n c l	Class	n c l
	Alfalfa	54	Oats	20
	Corn-notill	1434	Soybeans-notill 968
	Corn-min	834	Soybeans-min	2468
	Corn	234	Soybeans-clean 614
	Grass/Pasture	497	Wheat	212
	Grass/Trees	747	Woods	1294
	Grass/Past.-mowed	26	Towers	95
	Hay-windrowed	489	Other	380
			Total	10366

Table 2 :

 2 Classes and Samples (n c l ) of the ground truth of the Indian Pines 2010 dataset (cf. Fig.3).

	Class	n c l	Class	n c l
	Corn-high	3387	Hay	50045
	Corn-mid	1740	Grass/Pasture	5544
	Corn-low	356	Cover crop 1	2746
	Soy-bean-high	1365	Cover crop 2	2164
	Soy-bean-mid	37865	Woodlands	48559
	Soy-bean-low	29210	Highway	4863
	Residues	5795	Local road	502
	Wheat	3387	Buildings	546
			Total	198074

Table 3 :

 3 Classes and Samples (n c l ) of the ground truth of the Houston 2013 dataset (cf. Fig.4).

	Class	n c l	Class	n c l
	Healthy grass	1231	Road	1219
	Stressed grass	1196	Highway	1224
	Synthetic grass 697	Railway	1162
	Trees	1239	Parking Lot 1	1233
	Soil	1152	Parking Lot 2	458
	Water	325	Tennis Court	428
	Residential	1260	Running Track 660
	Commercial	1219	Total	14703

c) Houston 2013 (CASI spectrometer VHR HS + LiDAR data).

Table 4 :

 4

		Filter	θ
	Morphological	Opening / closing Top-hat opening / closing Opening / closing by re-construction Opening / closing by re-	Bi, s, α Bi, s, SE, α Bi, s, SE, α Bi, s, SE, α
		construction top-hat
	Texture	Average Entropy Standard deviation Range	Bi, s Bi, s Bi, s Bi, s
	Attribute	Area Bounding box diagonal	Bi, Area threshold Bi, Diagonal threshold
	Ratios	Simple Normalized	Bi/Bj (Bi -Bj)/(Bi + Bj)

Filters considered in the experiments (Bi, Bj: input bands (i, j ∈ [1, . . . b]); s: size of moving window, SE : type of structuring element; α: angle).

Table 5 :

 5 Results by MLC classifiers trained with the spectral bands (ω), with spatial features extracted from the three first PCAs (s, including morphological and attribute filters) or with the proposed active set. In the HOUSTON 2013 case, features extracted from the DSM have also been added to the input space. using the complete set of 14'627 morphological and attribute features extracted from each spectral band (result not reported in the table) 3 . On the two other datasets, the AS-BANDS method provided average Kappa of 0.98 and 0.92, respectively.

	Method	Ω PINES 1992 PINES 2010 HOUSTON 2013
	MLC-ω	ℓ1 0.42 ± 0.02 0.58 ± 0.01	0.61 ± 0.01
	# features	60 ± 3	107 ± 9	54 ± 3
	MLC-ω	ℓ2 0.59 ± 0.03 0.90 ± 0.01	0.80 ± 0.01
	# features	200	360	145
	AS-BANDS ℓ1ℓ2 0.83 ± 0.02 0.98 ± 0.01 0.92 ± 0.01
	# features	96 ± 5	68 ± 5	71 ± 3
	MLC-s	ℓ1 0.85 ± 0.02 0.84 ± 0.01	0.76 ± 0.01
	# features	85 ± 7	64.2 ± 3	82 ± 5
	MLC-s	ℓ2 0.85 ± 0.01 0.96 ± 0.01	0.88 ± 0.01
	# features	217	228	303
	AS-PCAS ℓ1ℓ2 0.89 ± 0.03 0.99 ± 0.01 0.92 ± 0.01
	# features	82 ± 4	83 ± 8	64 ± 4

In this paper, we refer to classification as the process of attributing one land cover type (type of material) or land use (use of the land, e.g., road vs parking) type to each pixel in the scene.

http://spams-devel.gforge.inria.fr/

Only squared structuring elements were used and the filter size range was pre-defined by expert knowledge.

First row: RGB subsets; Second row: ground truth; Third row: output of the classification with the proposed approach; Fourth row to end: visualization of the six features with highest squared weights.


J.-M Fontmorin

J Siguie

F Fourcade

F Geneste

D Floner

I Soutrel

A Amrane

Combined electrochemical treatment / biological process for the removal of a commercial herbicide solution, U46D ®

Keywords: Commercial pesticide U46D ®, Combined process, Electrochemical process, Biological treatment, Activated sludge

compounds generated during the electrochemical pretreatment in small concentration was therefore shown.

Abstract

The removal of a commercial solution of 2,4-D, U46D ® , was carried out by coupling an electrochemical oxidation and a biological process involving activated sludge.

The similar electrochemical behavior of 2,4-D and U46D ® highlighted their oxidation around 1.6 V/SCE and the feasibility of an electrochemical pretreatment. It was based on a homemade flow cell involving bare graphite felt electrode. To propose a consistent mechanism for 2,4-D oxidation, the indirect determination of • OH has been performed and the absence of radicals formation during 2,4-D electrolysis was confirmed. Consequently, the proposed pretreatment can be considered as a 'direct' electrochemical process instead of an advanced electrochemical oxidation process.

The impact of the flow rate on the pretreatment showed that 3 mL min -1 was a good compromise between the pretreatment time and the electrolysis efficiency, since it led to an almost total degradation of the pollutant while its mineralization remained limited. At this flow rate and for 500 mg L -1 of 2,4-D, the energy cost was estimated at 5 kWh m -3 . The biodegradability of U46D ® solution was not significantly modified after electrolysis, most likely due to the presence of dimethylamine salt in U46D ® . Owing to the significant BOD 5 /COD ratio measured, a biological treatment of the commercial U46D ® solution was however considered.

The electrochemical pretreatment shortened the duration of the biodegradation. For nonpretreated U46D ® (100 mg L -1 2,4-D), mineralization remained limited until 6 days of culture (33.7% DOC removal), and total removal of the DOC was observed after 8 days. For pretreated U46D ® , 63.7% decrease until the fifth day of culture was observed but total mineralization was not reached at the end of culture (72.1%). An overall mineralization yield during the coupled process of 82.1% was therefore reached. The presence of refractory

Introduction

Electrochemical processes for pesticides removal have been mainly investigated from the nineties. The adaptability and applicability to different volumes, media and pollutants as well as the possibility and ease of automation (current intensity or electrode potential are easy to control) constitute obvious advantages of electrochemical processes [START_REF] Chen | Electrochemical technologies in wastewater treatment[END_REF][START_REF] Rajeshwar | Electrochemistry and the environment[END_REF][START_REF] Jüttner | Electrochemical approaches to environmental problems in the process industry[END_REF]. Moreover, these processes can be applied to flow systems [START_REF] Malpass | Oxidation of the pesticide atrazine at DSA ® electrodes[END_REF].

Concerning pesticides removal, three main processes are studied: electro-coagulation, electrooxidation and electro-Fenton.

Electro-coagulation is based on the soluble anodes principle. When current intensity or potential is generated between two electrodes (in general iron or aluminum electrodes), the corresponding ions are produced which entails the generation of coagulants in solution and then the coagulation-flocculation of pollutants [START_REF] Liu | Electrocoagulation in water treatment[END_REF]. For example, when a sacrificial iron anode is implemented, the produced ferrous ions are oxidized to ferric ions which precipitate into Fe(OH) [START_REF] Jüttner | Electrochemical approaches to environmental problems in the process industry[END_REF] . The metallic hydroxide is produced in order to reach a sufficient concentration to initiate polymerization or condensation reactions [START_REF] Cengiz Yatmaz | Degradation of pesticide monochrotophos from aqueous solutions by electrochemical methods[END_REF][START_REF] Abdel-Gawad | Removal of some pesticides from the simulated waste water by electrocoagulation method using iron electrodes[END_REF]. Fine hydrogen and oxygen bubbles produced at the cathode and anode surfaces, respectively drag particles in suspension to solution surface by electroflotation. This process is effective in mild conditions (of temperature and turbidity) unlike in chemical coagulation. However, electro-coagulation being a non-degradative process, produced flocs should be removed by decantation, flotation or filtration. Moreover, the anodes have to be regularly replaced and electrodes passivation may occur limiting the process efficiency [START_REF] Yildiz | Electrocoagulation of synthetically prepared waters containing high concentration of NOM using iron cast electrodes[END_REF]. An enhanced electro-coagulation method (peroxy-coagulation) was reported by Brillas et al. [START_REF] Brillas | Degradation of 4-chlorophenol by anodic oxidation, electro-Fenton, photoelectro-Fenton, and peroxy-coagulation process[END_REF]. This method is partially degradative and thus reduces the volume of produced sludge. Coagulation occurs simultaneously with Fenton's reaction between Fe 2+ ions and cathodically produced H 2 O 2 .

The anodic oxidation or electro-oxidation is an Electrochemical Advanced Oxidation Process (EAOP) based on the production of a strong oxidant, the hydroxyl radical M( • OH).

The mineralization efficiency depends on three crucial factors: the hydroxyl radical generation, the nature and the stability of the electrode material and the competition of oxygen release. Adsorbed hydroxyl radicals are generated at the anode surface via water oxidation, and interactions between hydroxyl radicals and the anode surface depend on the electrode material. The electrode is considered as "active" when this interaction is strong and characterized by a low oxygen overpotential; it is the case for IrO 2 based electrodes [START_REF] Comminelis | Electrocatalysis in the electrochemical conversion/combustion of organic pollutants for waste water treatment[END_REF]. In this case, oxygen release is favored at the expense of reactions between adsorbed hydroxyl radicals and organic compounds [START_REF] Bard | Electrochemical Engineering[END_REF] and then mineralization yields are reduced. In the case of strong oxygen overpotential, the oxidation of organic compounds by hydroxyl radicals is favored near the electrode surface, and consequently, the electrode is considered as "nonactive". BDD is one of the best material for this type of electrode due to its large O 2 evolution overpotential and high electrochemical stability [START_REF] Fryda | Properties of diamond electrodes for wastewater treatment[END_REF] and was thus largely studied [START_REF] Boye | Electrochemical incineration of chloromethylphenoxy herbicides in acid medium by anodic oxidation with boron-doped diamond electrode[END_REF][START_REF] Alves | A comparative study of the electrochemical oxidation of the herbicide tebuthiuron using boron-doped diamond electrodes[END_REF][START_REF] Flox | Electrochemical combustion of herbicide mecoprop in aqueous medium using a flow reactor with a boron-doped diamond anode[END_REF][START_REF] Polcaro | Case studies in the electrochemical tretament of wastewater containing organic pollutant using BDD[END_REF]. Pt, PbO 2 , SnO 2 electrodes have been also extensively investigated [START_REF] Panizza | Electrocatalytic materials for the electrochemical oxidation of synthetic dyes[END_REF][START_REF] Polcaro | On the performance of Ti/SnO2 and Ti/PbO2 anodesin electrochemical degradation of 2-chlorophrnolfor wastewater treatment[END_REF][START_REF] Ghalwa | Electrochemical degradation of linuron in aqueous solution using Pb/PbO 2 and C/PbO 2 electrodes[END_REF]. For all these studies, mineralization yields exceeded 75% and reached up to 100% for current densities from 75 to 150 mA cm -2 .

Other electrode materials have also been used for pesticide removal, such as Dimensionally Stable Electrode (DSA), which are constituted of a titanium support covered by a conductive film of metallic oxide or metallic oxides alloys. According to the nature of the conductive film, they can be "active" or "non-active" [START_REF] Trasatti | Electrocatalysis: understanding the success of DSA ®[END_REF][START_REF] Panizza | Importance of electrode material in the electrochemical treatment of wastewater containing organic pollutants[END_REF]. In order to increase the mineralization yields, sodium chloride can be added to the electrolyte solution for the production of new oxidant such as Cl 2 or ClO 2 ; this indirect oxidation can be carried out as an alternative to the direct production of hydroxyl radicals [START_REF] Malpass | Oxidation of the pesticide atrazine at DSA ® electrodes[END_REF][START_REF] Neto | Electrooxidation of glyphosate herbicide at different DSA ® compositions: pH, concentration and supporting electrolyte effect[END_REF].

In the electro-Fenton process, hydrogen peroxide is generated in situ by the two electrons reduction of oxygen at the cathode surface, as well as the regeneration of ferrous ions. Compared to a classical Fenton process, the in situ production of reactants prevents from their consumption in large amounts and the regeneration of the catalyst avoids its accumulation as ferric ions, thus decreasing the formation of sludge. At optimized conditions there is no sludge formation since Fe 3+ does not precipitate at pH 3 for a concentration lower than 0.1 mM, which is used in carbon felt version. Graphite felt cathodes [START_REF] Oturan | An ecologically effective water treatment technique using electrochemically generated hydroxyl radicals for in situ destruction of organic pollutant: application to herbicide 2,4 D[END_REF][START_REF] Oturan | Production of hydroxyl radicals by electrochemically assisted Fenton's reagent: application to the mineralization of an organic micropollutant, pentachlorophenol[END_REF][START_REF] Oturan | Degradation of chlorophenoxyacid herbicides in aqueous media, using a novel method[END_REF] and oxygen gas diffusion electrodes [START_REF] Brillas | Electrochemical destruction of chlorophenoxy herbicides by anodic oxidation and electro-Fenton using a boron-doped diamond electrode[END_REF][START_REF] Sirés | Catalytic behavior of the Fe 3+ /Fe 2+ system in the electro-Fenton degradation of the antimicrobial chlorophene[END_REF][START_REF] Flox | Electro-Fenton and photoelectro-Fenton degradation of indigo carmine in acidic aqueous medium[END_REF][START_REF] Sirés | Degradation of clofibric acid in acidic aqueous medium by electro-fenton et photoelectro-Fenton[END_REF] are the most commonly used electrodes. Various types of Fenton-like processes can be performed [START_REF] Rosales | Advances in the elctro-Fenton process for remediation of recalcitrant organic compounds[END_REF]; ferrous ions can be directly added to the solution or produced from the oxidation of a sacrificial iron anode (Fered-Fenton). Hydrogen peroxide can also be added to the solution or generated at the cathode surface.

Pesticide removal has been largely studied by the electro-Fenton process [START_REF] Vasudevan | Electrochemistry: as cause and cure in water pollution -an overview[END_REF] and mineralization yields are similar to those obtained by electro-oxidation [START_REF] Oturan | An ecologically effective water treatment technique using electrochemically generated hydroxyl radicals for in situ destruction of organic pollutant: application to herbicide 2,4 D[END_REF]. Electro-Fenton process allows some adaptations especially concerning electrode materials and cell design.

For example, a BDD anode can be added in the electrochemical cell to improve the mineralization rate thanks to a concomitant production of hydroxyl radicals [START_REF] Brillas | Electrochemical destruction of chlorophenoxy herbicides by anodic oxidation and electro-Fenton using a boron-doped diamond electrode[END_REF]. A very good example of the electro-Fenton process using a BDD anode was carried out by Oturan et al. [START_REF] Oturan | Unprecedent total mineralization of atrazine and cyanuric acid by anodic oxidation and electro-Fenton with a boron-doped diamond anode[END_REF] when they studied the mineralization of atrazine and cyanuric acid. More recently, the photoelectro-Fenton process was envisaged to increase the performances of electro-Fenton [START_REF] Flox | Electro-Fenton and photoelectro-Fenton degradation of indigo carmine in acidic aqueous medium[END_REF][START_REF] Sirés | Degradation of clofibric acid in acidic aqueous medium by electro-fenton et photoelectro-Fenton[END_REF][START_REF] Boye | Anodic oxidation, electro-Fenton and photoelectro-Fenton treatments of 2,4,5-trichlorophenoxyacetic acid[END_REF].

Electro-oxidation and electro-Fenton are efficient processes leading to high mineralization yields. In order to mineralize organic recalcitrant compounds, an alternative is the coupling of a physico-chemical treatment with a biological process. It can be interesting from an economical point of view since for this type of effluent, the use of an AOP is estimated at 10-20 m -3 at industrial scale, while the coupling of solar photo-Fenton treatment and biological process was recently estimated at 7 m -3 [START_REF] Comninellis | Advanced oxidation processes for water treatment: advances and trends for R& D[END_REF].

Several electrochemical processes have been carried out after a biological treatment. For example, Ti/RuO 2 -IrO 2 electrodes [START_REF] He | Advanced treatment of biogically pretreated coking wastewater by electrochemical oxidation using Ti/RuO 2 -IrO 2 electrodes[END_REF] or BDD [START_REF] Schmalz | Electrochemical desinfection of biologically treated wastewater from small treatment systems by using boron-doped diamond (BDD) electrodes -Contribution for direct reuse of domestic wastewater[END_REF] have been used for the disinfection of biological pretreated domestic effluent, and Ti/BDD was employed during a coke waste water post-treatment [START_REF] Zhu | Advanced treatment of biologically pretreated coking wastewater by electrochemical oxidation using boron-doped diamond electrodes[END_REF].

An electrochemical process can also be performed as a pre-treatment in order to increase effluent biodegradability and/or reduce its toxicity. To achieve the complete mineralization of the effluent at the end of the combined process, the decrease of DOC should remain limited during this electrochemical pretreatment step. Indeed, significant residual organic carbon should remain available as a carbon substrate for microorganisms during the subsequent biological process [START_REF] Scott | Integration of chemical and biological processes for water treatment: review and recommendations[END_REF][START_REF] De | Integrated process for degradation of amitrole in wastewaters: photocatalysis/biodegradation[END_REF]. In order to increase industrial effluents biodegradability, various electrode materials have been studied such as platinum [START_REF] Torres | Electrochemical treatment of industrial wastewater containing 5-amino-6-methyl-2-benzimidazolone: toward an electrochemical-biological coupling[END_REF], stainless steel [START_REF] Doan | Combined electrochemical and biological treatment of industrial wastewater using porous electrodes[END_REF], Ti/RuO 2 -IrO 2 [START_REF] Babu | Treatment of dye effluent by electrochemical and biological processes[END_REF][START_REF] Antony | Optimization of integrated electro-bio process for bleaching effluent treatment[END_REF][START_REF] Basha | Electro-degradation and biological oxidation of non-biodegradable organic contaminants[END_REF] or Pb/PbO 2 [START_REF] Yahiaoui | Combination of an electrochemical pretreatment with a biological oxidation for the mineralization of nonbiodegradable organic dyes: basic Yellow 28 dye[END_REF]. Similarly, electro-oxidation and electro-Fenton have been studied as pretreatments prior to a biological process [START_REF] Babu | Treatment of dye effluent by electrochemical and biological processes[END_REF][START_REF] Basha | Electro-degradation and biological oxidation of non-biodegradable organic contaminants[END_REF][START_REF] Yahiaoui | Combination of an electrochemical pretreatment with a biological oxidation for the mineralization of nonbiodegradable organic dyes: basic Yellow 28 dye[END_REF][START_REF] Yahiaoui | Removal of tetracycline hydrochloride from water based on direct anodic oxidation (Pb/PbO 2 electrode) coupled to activated sludge culture[END_REF][START_REF] Khoufi | Pilot scale hybrid process for olive mill wastewater treatment and reuse[END_REF]. With an integrated system, the risk is to decrease the efficiency of the biological treatment [START_REF] Liu | Integrated biological and electrochemical oxidation treatment for high toxicity pesticide pollutant[END_REF], because the production of strong oxidant such as hydroxyl radical can inhibit microorganisms metabolism. In order to prevent this inhibition, both processes can be separated, for instance in the case of Acetomipride (1000 mg L -1 ) removal; it was first oxidized with a BDD anode and then biologically treated by means of an activated sludge biofilter, with the solution circulating from one to the other [START_REF] Liu | Integrated biological and electrochemical oxidation treatment for high toxicity pesticide pollutant[END_REF].

In order to improve biodegradability in view of a subsequent biological treatment, namely when total mineralization of the effluent is not needed, direct oxidation or reduction of recalcitrant compounds on an active electrode such as graphite felt can also be considered, even though the corresponding literature remains scarce [START_REF] Alonso Salles | Relevance of en electrochemical process prior to a biological treatment for the removal of an organophosphorous pesticide, phosmet[END_REF][START_REF] Belkheiri | Feasability of an electrochemical pretreatment prior toa biological treatment for tetracycline removal[END_REF][START_REF] Fontmorin | Electrochemical oxidation of 2,4-dichlorophenoxyacetic acid: analysis of by-products and improvement of the biodegradability[END_REF][START_REF] Fontmorin | Combined process for 2,4-dichlorophenoxyacetic acid treatment -Coupling of an electrochemical system with a biological treatment[END_REF]. The aim is to better target the electrochemical reaction, which would be useful not only from an economical point of view, but also in order to control the formation of by-products. A more selective pre-treatment could thus prevent from the formation of more toxic compounds, as it has already been shown in some AOPs and EAOPs processes [START_REF] Fontmorin | Electrochemical oxidation of 2,4-dichlorophenoxyacetic acid: analysis of by-products and improvement of the biodegradability[END_REF]. Such a direct electrode reaction led to promising results for 2,4-D, a chlorinated phenoxy acid herbicide [START_REF] Fontmorin | Electrochemical oxidation of 2,4-dichlorophenoxyacetic acid: analysis of by-products and improvement of the biodegradability[END_REF][START_REF] Fontmorin | Combined process for 2,4-dichlorophenoxyacetic acid treatment -Coupling of an electrochemical system with a biological treatment[END_REF]. Indeed, we have previously shown that mild oxidation of the target compound can be sufficient to improve the biodegradability of 2,4-D solutions [START_REF] Fontmorin | Electrochemical oxidation of 2,4-dichlorophenoxyacetic acid: analysis of by-products and improvement of the biodegradability[END_REF] allowing a subsequent biological treatment [START_REF] Fontmorin | Combined process for 2,4-dichlorophenoxyacetic acid treatment -Coupling of an electrochemical system with a biological treatment[END_REF]. To complete this previous work and to approach real conditions, the removal of a commercial solution of 2,4-D named U46D ® has been tested by mild oxidation coupled with activated sludge treatment.

Materials and Methods

Chemicals

2,4-dichlorophenoxyacetic acid (2,4-D) (98%) was purchased from Alfa Aesar (Schiltigheim, France) and U46D ® was commercialized by SAS Nufarm (Genneviliers, France). Chlorohydroquinone (85%) was purchased from Sigma-Aldrich (Saint-Quentin Fallavier, France). Acetonitrile (ACN) and formic acid were LC/MS grade from JT Baker (Deventer, Netherlands). All standards were prepared with ultra pure water (PurelabOptions-Q7/15, Elga, 18.2 MΩ cm).

Electrochemical pre-treatment

The electrochemical pre-treatment was based on a home-made flow cell [START_REF] Fontmorin | Electrochemical oxidation of 2,4-dichlorophenoxyacetic acid: analysis of by-products and improvement of the biodegradability[END_REF]. Graphite felt used as working electrode was supplied by Le Carbone Lorraine (RVG 4000 -Mersen, Paris La Défense, France). Its specific area, measured by the BET method, was 0.7 m² g -1 ; its density was 0.088 g cm -3 and its carbon yield was 99.9%.

Two interconnected stainless steel plates were used as counter-electrodes and compartments were separated by cationic exchange membranes (Ionac 3470 -Lanxess SAS, Courbevoie, France). The reference electrode (Saturated Calomel Electrode -SCE) was positioned in the middle of the graphite felt, and the potential control was performed using a VersaSTAT 3 potentiostat from Ametek/Princeton Applied (Elancourt, France). To ensure a good homogeneity of the potential distribution in the three dimensional working electrode, the felt was located between the two counter-electrodes [START_REF] Moinet | Electrosynthèse en continu. Méthodes directes et indirectes[END_REF]. The cell was thoroughly rinsed with distilled water before and after each experiment. The electrolyte solution percolated the porous electrode and flow rate was monitored by a Gilson minipuls 2 peristaltic pump (Middleton, WI, USA).

Biological process

Activated Sludge used in this study was collected from a local wastewater treatment plant in Rennes (Rennes Beaurade, France). It was washed several times by a succession of centrifugations. After each washing, activated sludge was centrifuged at 3000 rpm for five minutes (Jouan, Thermo Fisher Scientifics, Saint Herblain, France). The supernatant was then separated from the sludge to remove any residual carbon or mineral source.

Culture medium was prepared in triplicate in 500 mL Erlenmeyer flasks containing 250 mL of either pre-treated solutions or pure U46D ® solutions. Minerals were spiked in the medium as highly concentrated solutions to reach the following initial composition (mg L -1 ): FeCl 3 .6H 2 O, 0.26; and the initial pH was adjusted to 7.0 ± 0.2. Activated sludge was added in order to achieve an initial concentration of 0.5 g L -1 of dry matter. Cultures were agitated at 250 rpm, and kept at 30°C in aerobic conditions. Samples (5 mL) were taken regularly and filtered through 0.45 µm-syringe filters for measurements.

Analysis

Measurements of the residual 2,4-D and chlorohydroquinone concentration were performed by HPLC (High Performance Liquid Chromatography) (Milford, USA) system involving a pump Waters 600, fitted with a Phenomenex Kinetex ® C18 2.6 µm column (4.6 mm × 100 mm), along with a Waters 996 Photodiode array detector, a Waters 717 ph Autosampler and controlled through an Empower ® 2 program. The mobile phase consisted of acetonitrile and trifluoroacetic acid (TFA) 0.1% in ultra-pure water with a ratio of 30/70 at 1 mL min -1 . GC/MS/MS measurements were performed with a Perkin Elmer Clarus 500 apparatus coupled with a Variance CB-FFAP 0.25 µm column (25 m × 0.15 mm). Compounds identification was performed with the help of a List database.

Dissolved Organic Carbon (DOC) measurements

Solutions were filtered on Satorius Stedim Minisart 0.45 µm GF prefilters (Goettingen, Germany). DOC was measured by means of a TOC-V CPH/CPN Total Organic Analyzer Schimadzu. Organic carbon compounds were combusted and converted to CO 2 , which was detected and measured by a non-dispersive infrared detector (NDIR).

Reproducible DOC values were always obtained using the standard NPOC (Non Purgeable Organic Carbon) method.

Chemical Oxygen

Results and Discussion

Electrochemical pretreatment

GC/MS analysis showed that the commercial herbicide U46D ® was mainly composed of 2,4-D and dimethylamine salt (data not shown). Other compounds were detected at trace levels, such as 2,4-dichlorophenol or xylene (dimethyl benzene).

Previous study [START_REF] Fontmorin | Electrochemical oxidation of 2,4-dichlorophenoxyacetic acid: analysis of by-products and improvement of the biodegradability[END_REF] highlighted that 2,4-D can be oxidized at 1.6 V/SCE on graphite felt electrode. Figure 1 An electrochemical pretreatment of the commercial 2,4-D solution was therefore performed and was first investigated at 1.6 V/SCE with 1 mL min -1 flow rate . Electrolysis was then carried out at a constant current intensity of 200 mA, which corresponds to the current intensity measured during the electrolyses at 1.6 V/SCE. Working at a constant current intensity would be helpful for an industrial purpose, since at large scale the reference electrode cannot be used. After electrolysis, high elimination yield, 95.5%, was obtained in only one pass through the flow-cell, similar to those obtained for pure 2,4-D solutions [START_REF] Fontmorin | Electrochemical oxidation of 2,4-dichlorophenoxyacetic acid: analysis of by-products and improvement of the biodegradability[END_REF].

This result confirmed the feasibility of the electrochemical pre-treatment and was promising for the electrolysis of high amounts of pesticides since the recycling of the solution was not needed.

Nature of the electrochemical process

The production of hydroxyl radicals is closely linked to the electrode material used and then to oxygen overvoltage. Mechanisms involving • OH have been previously proposed in studies based on electrochemical oxidation on graphite felt [START_REF] Polcaro | Electrochemical oxidation of chlorophenols[END_REF]. If some electrodes (boron doped diamond for instance) are well-known to be powerful candidates for • OH generation, from a thermodynamic point of view, the formation of hydroxyl radicals on graphite felt seems unlikely for potentials lower than 2 V/SCE. Indeed, with a standard potential of 2.80 V/SHE for • OH/H 2 O, the oxidation potential at pH 7 reaches 2.38 V/SHE (decrease of 0.06 V per unit of pH) i.e. 2.13 V/SCE. Therefore, in order to conclude on the electrochemical oxidation phenomena occurring during the use of such material and to confirm the specificity and selectivity of the considered process, namely a direct oxidation at the electrode surface, the indirect determination of the hydroxyl radicals has been realized.

• OH are known for their strong reactivity with benzene (kinetic rate constant k = 8 10 9 L mol - 1 s -1 ), showing the relevance of this compound as a radicals' scavenger [START_REF] Kochany | Mechanism of photodegradation of aqueous organic pollutants. 2. Measurement of the primary rate constants for reaction of hydroxyl radicals with benzene and some halobenzenes using an EPR spin-trapping method following the photolysis of hydrogen peroxide[END_REF][START_REF] Pan | Oxidation of benzene by the OH radical. A product and pulse radiolysis study in oxygenated aqueous solution[END_REF]. Since phenol is the sole product of benzene hydroxylation, it is possible to deduce the amount of • OH produced from the monitoring of benzene degradation (λ=200 nm) linked to phenol production (λ=270 nm).

From this, and after checking the absence of direct benzene oxidation at the electrode at 1.6 V/SCE, electrolysis was performed (20 mM initial benzene concentration) in the flow-cell with recycling of the electrolyzed solution. Neither noticeable benzene degradation nor phenol formation could be observed after 140 min of electrolysis. This result clearly confirmed that in the considered experimental conditions and electrolysis time, oxidation of water on graphite felt did not result in • OH formation. Furthermore and contrarily to electrolysis of the 2,4-D or U46D ® solutions, the recycling of the electrolysis solution through the cell was considered for this experiment, which was in favor of phenol formation from benzene hydroxylation, confirming unambiguously the absence of radicals formation during 2,4-D electrolysis. Consequently, the proposed pretreatment can be considered as a 'direct' electrochemical process instead of an advanced electrochemical oxidation process.

Impact of the flow rate on pretreatment efficiency

A flow rate of 1 mL min -1 was previously considered owing to the high degradation yields obtained if compared to those observed at higher flow rates [START_REF] Alonso Salles | Relevance of en electrochemical process prior to a biological treatment for the removal of an organophosphorous pesticide, phosmet[END_REF][START_REF] Belkheiri | Feasability of an electrochemical pretreatment prior toa biological treatment for tetracycline removal[END_REF]. The percolation flow rate necessarily impacts the time-contact of the effluent with the polarized surface of the graphite felt. The impact of the percolated flow rate on pretreatment efficiency was therefore investigated, at a constant potential and then at a constant current intensity.

Chronoamperometry study 2,4-D degradation

As seen above, electrolysis of 2,4-D performed at 1.6 V/SCE with 1 mL min -1 flow rate led to 96% degradation with only a single pass through the cell [START_REF] Fontmorin | Electrochemical oxidation of 2,4-dichlorophenoxyacetic acid: analysis of by-products and improvement of the biodegradability[END_REF]. To check the influence of the flow rate on the degradation yield, it was examined in the range 1 to 10 mL min -1 (Table 1 Until 3 mL min -1 , more than 99% degradation of the 2,4-D present in the commercial herbicide U46D ® was observed, and decreased to 83% for 5 mL min -1 and until only 46% for 10 mL min -1 .

The decrease in the yield is related to insufficient residence time in the graphite felt with less contact of electro-active species with the electrode surface. Indeed, since the reaction involves a rapid electron transfer between the substrate and the electrode, an increase of the flow rate probably leads to the apparition of preferential flow paths through the felt, owing to its high porosity. Therefore, the decrease of the degradation yield with the flow rate is most likely linked to the low contact between the solution and the electrode fibers, rather than slow reaction kinetics.

A flow rate of 3 mL min -1 appeared therefore to be a good compromise between the time of electrolysis, linked to the energy cost, and the efficiency. However, and to confirm the relevance of this flow rate, other parameters should be examined, such as mineralization, oxidation, by-products toxicity and biodegradability improvement.

Mineralization and oxidation

Impact of the flow rate of percolation on mineralization and oxidation at 1.6 V/SCE is displayed in table 1, showing that DOC and COD removal followed similar trends (table 1).

Mineralization decreased significantly when the flow rates increased from 1 to 2 mL min -1 (from 54.2 to 38.7%) and then the decrease was less pronounced; the same trend was observed for oxidation (from 54.1 to 35.6 %). These trends differed from that observed for 2,4-D, for which total degradation was observed until 3 mL min -1 (table 1), thus showing the impact of the flow rate on the formed by-products. From this, a flow rate of 3 mL min -1 still appeared to be a good compromise between the pretreatment time and electrolysis efficiency, since it led to an almost total pollutant degradation while mineralization remained limited, both results being in favor of a subsequent biological treatment.

Chronopotentiometry study

Although the electrolyses performed at a constant potential were helpful regarding specificity

(choice of a relevant potential related to the targeted compounds), it can present some drawbacks regarding possible subsequent industrial application. Indeed, it requires the use of a reference electrode, which is fragile and whose potential can vary with time (salt saturation, porous glass tip fouling…); in addition, with high surface 3-D electrodes, the potential homogeneity can appear difficult to control within all the electrode volume.

Chronopotentiometry is therefore needed in view of a possible subsequent industrial implementation, since it does not require a reference electrode, but the problem of the homogenous potential distribution within the electrode volume remains. Indeed, when one counter-electrode is used, the potential field lines decrease rapidly within the felt and hence the reaction occurs only at the electrode surface in front of the counter-electrode. In the flow cell, the two counter-electrodes are positioned each side of the porous electrode, allowing a better linearization of the potential distribution [START_REF] Moinet | Electrosynthèse en continu. Méthodes directes et indirectes[END_REF]. To work in similar conditions as in the previously described chronoamperometry experiments, the electrode potential was measured and the current intensity adjusted to obtain a potential close to 1.6 V/SCE, leading to applied current values of 200-250 mA.

Several electrolyses were performed at various flow rates (Fig. 2). As observed, and similarly to the behavior recorded in potentiostatic mode, high 2,4-D degradation yields were obtained until 3 mL min -1 (above 95%), while they decreased above this flow rate (68 and 28% at 5 and 10 mL min -1 , respectively) (Fig. 2a). As observed in potentiostatic mode, DOC and COD removal followed similar trends:

significant decrease of the levels of mineralization and oxidation were observed for 5 and 10 mL min -1 (Figs. 2b andc). Therefore, chronopotentiometry can be applied as an electrochemical pretreatment of 2,4-D; and confirming the above results obtained in potentiostatic mode, 3 mL min -1 appeared again to be a good compromise. mA with a single pass through the cell.

Energy cost

For advanced electrochemical oxidation process, the estimation of energy costs is usually based on the COD removal (more especially for 80-85% COD removal) [START_REF] Muff | Electrochemical treatment of drainage water from toxic dump of pesticides and degradation by-products[END_REF]. For example, for an electro-oxidation process carried out with a BDD anode, energy cost varied from 91 to 347 kWh m -3 depending on the nature of the treated effluent, since initial COD was in the range 0.1 to 2 g L -1 [START_REF] Cañizares | Costs of the electrochemical oxidation of wastewaters: a comparison with ozonation and Fenton oxidation processes[END_REF]. For the same electrode material and an initial COD of 1.75 g L -1 , the energy cost has been estimated to 35 kWh m -3 [START_REF] Urtiaga | Integrated treatment of landfill leachates including electrooxidation at pilot plant scale[END_REF]. With a Pb/PbO 2 anode, Panizza et al. [START_REF] Panizza | Electrochemical process for the treatment of landfill leachate[END_REF] have estimated the operating costs at 90 kWh m -3 while they increased to 681 kWh m -3 with a three dimensional carbon bed electrode [START_REF] Rao | Kinetics of electrooxidation of landfill leachate in e three-dimensional carbon bed electrochemical reactor[END_REF].

In this work, the energy cost cannot be estimated from COD removal, since after one pass through the electrolytic cell, COD removal never reached 80% and was more around 30-40%. Considering total 2,4-D degradation after one pass through the graphite felt electrode

(2,4-D 500 mg L -1 , flow rate of 3 mL min -1 and applied current 220 mA), operating costs can be estimated to 5 kWh m -3 for the electrochemical pretreatment.

Biological treatment

Biodegradability assessment

Biodegradability of solutions of U46D ® electrolyzed at 200 mA and 3 mL min -1 flow rate was examined. Contrarily to pure 2,4-D for which the BOD 5 /COD ratio increased from 0.04 to 0.25 [START_REF] Fontmorin | Electrochemical oxidation of 2,4-dichlorophenoxyacetic acid: analysis of by-products and improvement of the biodegradability[END_REF], electrolysis did not improve the biodegradability of the commercial herbicide solution (Table 2). The initial BOD 5 /COD ratio of U46D ® may be related to the presence of dimethylamine salt, which is obviously more biodegradable than the target compound, 2,4-D, and hence improves the biodegradability of the commercial solution, if compared to pure 2,4-D. The final value nearly similar to that obtained after pure 2,4-D electrolysis may be related to the presence of similar by-products to those obtained after pure 2,4-D electrolysis [START_REF] Fontmorin | Electrochemical oxidation of 2,4-dichlorophenoxyacetic acid: analysis of by-products and improvement of the biodegradability[END_REF].

Even though the BOD 5 /COD ratio of 0.4 according to which an effluent can be considered as "easily biodegradable" [START_REF] Pulgarin | Strategy for the coupling of photochemical and biological flow reactors useful in mineralization of biorecalcitrant pollutants[END_REF][START_REF] Sarria | Recent developments in the coupling of photoassisted and aerobic biological processes for the treatment of biorecalcitrant compounds[END_REF] was not reached, a biological treatment of the commercial U46D ® solution may be considered owing to the significant ratio measured. Prior to activated sludge cultures, pretreatment in intentiostatic mode at 1 and 3 mL min -1 flow rate was therefore considered thereafter.

Solution BOD 5 /COD

Pure 2,4-D 0.04 [START_REF] Alonso Salles | Relevance of en electrochemical process prior to a biological treatment for the removal of an organophosphorous pesticide, phosmet[END_REF] Pure electrolyzed 2,4-D 0.25 [START_REF] Alonso Salles | Relevance of en electrochemical process prior to a biological treatment for the removal of an organophosphorous pesticide, phosmet[END_REF] Non-electrolyzed U46D ® solution 0.21 Electrolyzed U46D ® solution 0.21

Table 2: Assessment of the biodegradability of pure and electrolyzed 2,4-D, as well as those of the commercial herbicide U46D ® before and after electrolysis (100 mg L -1 2,4-D)

Biological treatment of non-pretreated U46D ®

Activated sludge cultures were carried out for two U46D ® concentrations, 100 and 500 mg L -1 (Fig. 3). A higher mineralization was observed in the beginning of the culture for 500 mg L -1 2,4-D, 33.7% mineralization after 6 days. A rapid DOC decrease was then observed until 8 days culture for both concentrations, until an almost total removal for 100 mg L -1 initial 2,4-D and 80% degradation for 500 mg L -1 2,4-D (Fig. 3a). DOC values then remained nearly constants until cessation of culture, showing that total mineralization could not be achieved The behavior observed for mineralization was confirmed from 2,4-D monitoring, showing a low target compound degradation until 5-6 days culture (Fig. 3b), which may be considered as an acclimation period; a high degradation rate was then observed until 8 days culture before an almost constant values until the end of culture. Final yields of 2,4-D degradation appeared to be similar to those of mineralization, with an almost total removal for 100 mg L -1 initial 2,4-D and 79% degradation for 500 mg L -1 2,4-D (Fig. 3b). It should be observed that biosorption, which is a rapid phenomenon, was most likely not involved in target compound removal owing to the low decrease observed during the first day of treatment, in agreement with previous results [START_REF] Fontmorin | Combined process for 2,4-dichlorophenoxyacetic acid treatment -Coupling of an electrochemical system with a biological treatment[END_REF].

If compared to those obtained during biological treatment of pure 2,4-D solutions, these results were confirmed owing to the similar behavior observed [START_REF] Fontmorin | Combined process for 2,4-dichlorophenoxyacetic acid treatment -Coupling of an electrochemical system with a biological treatment[END_REF]. The higher rates observed on the commercial solution are in agreement with the higher BOD 5 /COD ratios described above (0.04 and 0.21 for pure 2,4-D and U46D ® ). These results are most likely related to the presence of dimethylamine salt in the commercial formulation that could increase its "general biodegradability".

Biological treatment of pretreated U46D ®

Biological treatment was performed on the commercial U46D ® solution (100 mg L -1 2,4-D) pretreated at 200 mA, at flow rates of 1 and 3 mL min -1 (Fig. 4). As observed, similar behavior was observed for the two considered flow rates (Fig. 4a): after one day activated sludge culture, 13.1 and 15.6% DOC decrease were obtained for 1 and 3 mL min -1 flow rates, respectively. This decrease can be attributed to both biosorption and biodegradation phenomena. However, negligible biosorption of the main by-product, chlorohydroquinone, was previously demonstrated [START_REF] Fontmorin | Combined process for 2,4-dichlorophenoxyacetic acid treatment -Coupling of an electrochemical system with a biological treatment[END_REF], showing that biodegradation was mainly involved in DOC decrease. After 5 days culture, 60.3 and 63.7% DOC decrease were obtained for 1 and 3 mL min -1 flow rates, respectively (Fig. 4a). Low biodegradation rates were then observed until 70.4 and 72.1% DOC decrease obtained for 1 and 3 mL min -1 after 14 days culture (Fig. 4a), leading to overall mineralization yields during the coupled process of 81.3 and 82.1%, respectively.

These results should be compared to those recorded for non-pretreated U46D ® for which 98.3% mineralization was observed after 15 days culture, highlighting the persistence of refractory by-products in the pretreated solution, which remained even after 14 days activated sludge culture. As expected [START_REF] Fontmorin | Combined process for 2,4-dichlorophenoxyacetic acid treatment -Coupling of an electrochemical system with a biological treatment[END_REF], this refractory organic matter did not correspond to chlorohydroquinone, since for all studied flow rate, its total removal was observed after 9 days of culture (Fig. 4b), showing the presence of other refractory organic compounds accounting for the residual DOC.

If compared to those previously obtained on pure 2,4-D [START_REF] Fontmorin | Combined process for 2,4-dichlorophenoxyacetic acid treatment -Coupling of an electrochemical system with a biological treatment[END_REF], similar trends were observed for the commercial pesticide U46D ® . Indeed, the electrochemical pretreatment allowed a significant improvement of the mineralization kinetics and by-products degradation rates; however as also previously shown for 2,4-D, the persistence of low concentrated refractory by-products generated during the electrochemical pretreatment was noticed.

Conclusion

A combined process coupling an electrochemical pretreatment and a biological treatment was applied to a commercial herbicide solution, U46D ® , containing 2,4-D as an active molecule.

The electrochemical behavior of U46D ® was studied by cyclic voltammetry and a similar signal to that of the active molecule 2,4-D was recorded in oxidation around 1.6 V/SCE, confirming that 2,4-D is the major compound in the commercial herbicide. The absence of hydroxyl radicals formation during 2,4-D electrolysis was confirmed suggesting that 2,4-D reacted directly at the electrode surface. Consequently, the proposed pretreatment can be considered as a 'direct' electrochemical process instead of an advanced electrochemical oxidation process. The impact of the flow rate on the pretreatment efficiency was examined in potentiostatic mode and then in amperometric mode to avoid the need for a reference electrode and hence to be more realistic in view of a future industrial implementation. A flow rate of 3 mL min -1 appeared to be a good compromise between the pretreatment time (energy cost) and electrolysis efficiency, since it led to an almost total pollutant degradation while mineralization remained limited. Contrarily to pure 2,4-D for which the BOD 5 /COD ratio increased, electrolysis did not improve the biodegradability of the commercial herbicide solution. However and even if the limit of biodegradability was not reached (0.4), a biological treatment of the commercial U46D ® solution was carried out owing to the significant BOD 5 /COD ratio measured (0.21).

Activated sludge cultures were then performed on the commercial pesticide U46D ® electrochemically pretreated or not. The electrochemical pretreatment allowed a significant improvement of the mineralization kinetics and by-products degradation rates; however the persistence of refractory by-products from the electrochemical pretreatment in small quantities was highlighted. To remedy the presence of recalcitrant compounds at the end of the electrochemical pretreatment and to degrade these by-products, possible solutions could be the recycling of the solution in the flow cell on the one hand and an acclimation step of the activated sludge to the electrolyzed solution on the other hand.

(BOD 5 )

 5 Demand (COD) and Biological Oxygen Demand after 5 daysChemical Oxygen Demand (COD) was measured by means of a Test Nanocolor ® CSB 160 from Macherey-Nagel (Düren, Germany). The amount of oxygen required for the oxidation of the organic and mineral matter at 164°C for 30 min was quantified after oxidation with K 2 Cr 2 O 7 at acidic pH and heating. BOD 5 measurements were carried out in Oxitop IS6 (WTW, Alès, France). Activated sludge provided by a local wastewater treatment plant (Rennes Beaurade, Bretagne, France) was used to inoculate duplicate flasks and the initial microbial concentration was 0.5 g L -1 . Control flasks containing easily biodegradable compounds (glucose and glutamic acid at 150 mg L -1 each) were run at the same time; and blank flasks, for which the sample was replaced by water, were used to deduce the biological oxygen demand corresponding to the endogenous respiration. Activated sludge solution volume to be added in each flask was deduced from the range of expected BOD 5 values, given by BOD 5 = COD/1.46. The following mineral basis was used for all experiments (g L -1 ): MgSO 4 .7H 2 O, 22.5; CaCl 2 , 27.5; FeCl 3 , 0.15; NH 4 Cl, 2.0; Na 2 HPO 4 , 6.80; KH 2 PO 4 , 2.80. Before use, KOH was added to achieve neutral pH (7.0 ± 0.2) and nitrification inhibitor (10 mg L -1 solution of N-Allylthiourea) was added.

  represents the voltammogram obtained with a solution of U46D ® (500 mg L -1 ) in Na 2 SO 4 0.1 M, showing similar electrochemical behavior in oxidation to that observed for pure 2,4-D.

Fig. 1 .

 1 Fig.1. Cyclic Voltammetry of U46D ® (500 mg L -1 , continuous line) in 0.1 M Na 2 SO 4 (dotted line), 100 mV s -1 , glassy carbon electrode (7 mm²).

Fig. 2a .

 2a Fig. 2a. Influence of the flow rate on 2,4-D degradation yield during electrolysis performed at

Fig. 2b .

 2b Fig. 2b. Influence of the flow rate on DOC removal during electrolysis performed at 200-250

Fig. 3 .

 3 Fig. 3. Time-courses of Dissolved Organic Carbon (a) and 2,4-D concentrations (b) during biological treatment of U46D ® corresponding to 500 (▲) and 100 (■) mg L -1 2,4-D.

Fig. 4 .

 4 Fig.4. Time-courses of Dissolved Organic Carbon (a) and chlorohydroquinone (b) during biological treatment of U46D ® corresponding to 100 mg L -1 2,4-D, pretreated at 200 mA at a flow rate of 1 (○) and 3 (□) mL min -1 .

  Na 2 HPO 4 , 334; K 2 HPO 4 , 208; KH 2 PO 4 , 85; CaCl 2 , 27.4; MgSO 4 .7H 2 O, 22.6; NH 4 Cl, 75;

Flow rate (mL min -1 )

  ).

		1	2	3	5	10
	2,4-D Degradation yield (%)	>99.9	>99.9	99.3	83.3	46.6
	Oxidation yield (%)	54.1	35.6	32.2	24.7	10.3
	Mineralization yield (%)	54.2	38.7	35.2	27.2	14.7

Table 1 :

 1 Influence of the flow rate on degradation (2,4-D decrease), oxidation (COD removal) and mineralization (TOC removal) yields during chronoamperometry study.

Highlights :

• Electrochemical/biodegradation combination was tested for pesticide removal.

• Total commercial pesticide U46D ® degradation and 81.2% mineralization were reached.

• A 'direct' electrochemical process was confirmed by the absence of • OH formation.

• Biodegradability of the pesticide solution was improved, flow rate was optimized. • The energy cost was found to be similar to those of Advanced Oxidation processes.