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This paper proposes a new method for wet snow detection using multitemporal SAR data. The proposed change detection method is primarily based on the comparison between two X band SAR images acquired during the accumulation (winter) and the melting (spring) seasons, in the French Alps. The new membership decision method is relying on the local intensity statistics of the SAR images by considering the backscattering ratio as a stochastic process: the probability that " the intensity ratio is smaller than the corresponding locally varying dry/wet snow threshold " is larger than a predefined confidence level. The proposed snow backscattering simulations indicate a far more complex relation between the backscattering properties of the two snow types, with respect to the conventional assumption of the augmented electromagnetic absorption associated to the wet snow. The most conclusive results are confirmed at X band by in situ measurements. We show that this complexity is mostly caused by the dominance of a different backscattering component for each type of snow, leading to a significant angular sensitivity. Therefore, we introduce a variable threshold matrix instead of an unique threshold: the threshold is expressed as a function of the local incidence angle (LIA) for each pixel. The proposed snow backscattering simulator (SBS) allows deriving the locally varying threshold as the ratio between dry and wet snow backscattering coefficients computed for the SAR system parameters (polarization, frequency), the local topography parameters (LIA) and for the a priori derived and spatially averaged target parameters (underlying ground and snow cover surface properties, snow grain size, underlying ground dielectric permittivity, snow wetness). The threshold varying with respect to the frequency and the choice of the winter season reference image, allow the proposed algorithm applicability in wider frequency range.

This paper presents a new methodological framework to assess the conformity of multivariate high-resolution SAR data in terms of asymptotic statistics. Three important statistical properties are studied by applying statistical hypotheses testing, successively: circularity, sphericity and spherical symmetry. Starting from the classical tests designed for the multivariate Gaussian case, these tests are extended to the Spherically Invariant Random Vector (SIRV) stochastic model. A zero-mean test is proposed for both Gaussian and SIRV stochastic processes. The link between the spherical symmetry property and the conformity to the SIRV model is established asymptotically by the specific structure of the quadricovariance matrix. Two high and very high resolution datasets are used to illustrate departures from the standard model assumptions: TerraSAR-X multi-pass InSAR and ONERA RAMSES POLSAR images. As well, the derived tests are applied on the appropriate synthetic dataset. The detection results are qualitatively and quantitatively analysed and some important inferences are drawn regarding these two datasets.

International audience; This paper deals particularly with the sensitivity of the wet snow backscattering coefficient on density change. The presented backscattering model is based on the approach used in the dry snow analysis, appropriately modified to account for the increased dielectric contrast caused by liquid water presence. It encircles our undertaking of simulating and analysing snow backscattering using fundamental scattering theories (IEM-B, QCA, QCA-CP). The wet snow parameters are chosen according to the area of the particular interest - the French Alps, while the choice of the SAR sensor parameters (frequency, polarization) is primarily conditioned by the initially settled goal - reaching qualitative conclusions concerning wet snow backscattering mechanism. Based on simulation results, we state the dominance of the snow pack surface backscattering component, causing the backscattering to be directly proportional to the volumetric liquid water content. This result is confirmed by the performed in situ measurements. We illustrate as well the decrease of this effect with the increase in operating frequency.

International audience; This paper introduces Independent Component Analysis (ICA) to the Incoherent Target Decomposition theory (ICDT) through the particular application - snow cover analysis. Given that the equivalence of the currently used eigenvalue decomposition and Principal Component Analysis (PCA) can be stated under certain constraints, the goal is to generalise ICDT in the context of Blind Source Separation (family of techniques comprising both PCA and ICA). This generalisation allows independent non-orthogonal backscattering mechanisms retrieval in case of non-Gaussian polarimetric clutter. The obtained independent target vectors are parametrized using the Target Scattering Vector Model (TSVM). The algorithm is applied on a distributed target - snow cover, and the obtained parameters are illustrated and appropriately interpreted using the Poincare sphere.

International audience; Polarimetric incoherent target decomposition aims in accessing physical parameters of illuminated scatters through the analysis of target coherence or covariance matrix. In this framework, Independent Component Analysis (ICA) was recently proposed as an alternative method to eigenvector decomposition to better interpret non-Gaussian heterogeneous clutter (inherent to high resolution SAR systems). In this paper a Monte Carlo approach is performed in order to investigate the bias in estimating Touzi's Target Scattering Vector Model parameters when ICA is employed. Simulated data and data from the P-band airborne dataset acquired by the Office National d'tudes et de Recherches Arospatiales (ON-ERA) over the French Guiana in 2009 in the frame of the European Space Agency campaign TropiSAR are taken into consideration.

This thesis comprises two research axes. The first, being rather methodological, consists of our efforts to answer some of the open questions in the POLSAR community, while the latter is sooner related to the specific application - the remote sensing of snow.Following the alternative statistical modelling of highly textured multivariate SAR datasets by means of SIRV model, we propose the appropriate assessment of, otherwise assumed, cir- cularity and sphericity parameters. The last is coupled with the spherical symmetry test, forming a method for the evaluation of SIRV statistical model suitability in the context of POLSAR data. Given the rejection rate, challenging circularity and sphericity appears to be justified, while SIRV model pertinence must be doubted in the regions characterized by strong deterministic scattering. Further on, as the highlight of this thesis, we propose a po- larimetric incoherent target decomposition, based on ICA and founded on the hypothesis of non-Gaussianity of POLSAR clutter. By exploiting the information contained in the higher statistical orders, this decomposition provides at the output a set of mutually independent (rather than only decorrelated), non-orthogonal target vectors. Unlike the first dominant com- ponent, which is nearly identical to the one estimated by the conventional ICTD counterpart, the second dominant component differs significantly, which, as we anticipate, represents an additional potential for the POLSAR datasets interpretation.In the applied context, the first presented contribution would be a stochastic approach in snow mapping by means of multitemporal SAR datasets. The most notable supplements of the presented method to the ensemble of change detection techniques in snow mapping are the plausibly modified assumption of the wet/dry snow backscattering ratio and implicitly introduced spatial correlation between wet snow areas, achieved by directly implicating local speckle statistics in the decision process. Finally, we present the non-autonomous method for SWE spatial estimation, based on optical datasets. By successfully involving the remote sensing datasets in the calibration of the external SWE model, we seek to demonstrate the utility and the necessity of the former in the snow pack monitoring.; Cette thèse est composée de deux axes de recherche. Le premièr, plutôt méthodologique, consiste de nos efforts pour répondre à des questions ouvertes dans la communauté de RSO polarimétrique, tandis que le second est plutôt lié à l'application spécifique - le télédétection du manteau neigeux.Suite à la modélisation statistique alternative des images RSO multivariées et haute- ment texturées, par le modèle SIRV, nous proposons d'abord une évaluation appropriée des paramètres de circularité et sphéricité, autrement à priori présumés. La dernière est accouplée avec le test de symétrie sphérique, ce qui forme une méthode pour l'évaluation de pertinence de modèle statistique SIRV dans le contexte de données RSO polarimétriques. Compte tenu du taux de réjection, cela parait justifié de mettre en question les hypothèses de circularité et sphéricité, alors que la pertinence de modèle SIRV doit être soupçonnée dans les régions caractérisées par la diffusion déterministe forte. En suite, comme le point culminant de cette thèse, nous proposons une décomposition incohérente de cible polarimétrique, basée sur l'ACI et fondée sur l'hypothèse de non-Gaussianité du clutter RSO polarimétrique. En exploitant l'information contenue dans les ordres statistiques supérieurs, cette décomposition donne à la sortie un ensemble de vecteurs de cible, qui sont mutuellement indépendants (plutôt que seule- ment décorrélés) et non-orthogonaux. Contrairement à la première composant dominante, qui parait presque identique à l'une estimée par le homologue conventionnel, la deuxième com- posante dominante diffère significativement, ce qui représente un potentiel additionnel pour l'interprétation des données RSO polarimétriques.Dans le contexte appliqué, la première contribution présentée serait une approche stochas- tique pour la cartographie du manteau neigeux au moyen de données RSO multi-temporelles. Les apports les plus notables de la méthode présentée à l'ensemble de techniques de la détec- tion de changements dans la cartographie du manteau neigeux, sont l'hypothèse modifiée du ratio de rétrodiffusion entre la neige sèche et la neige humide, aussi que la corrélation spatiale entre les régions de la neige humide, introduite en impliquant la statistique locale de speckle dans le processus de décision. Finalement, nous présentons la méthode non-autonome pour l'estimation spatiale de l'équivalent en eau de la neige (EEN), basée sur des données optiques. En utilisant avec succès des données de télédétection dans la calibration du modèle EEN externe, on essaie de démontrer l'utilité et la nécessite du télédétection dans la surveillance du manteau neigeux.; Ova teza podrazumijeva dva pravca istraživanja. Prvi, metodološki, predstavlja naše naporeda odgovorimo na neka od otvorenih pitanja u POLSAR zajednici, dok je drugi vezan zaspecifičnu aplikaciju - teledetekciju sniježnog pokrivača.Uzevši u obzir alternativno statističko modelovanje multivarijantnih SAR slika visoke rezolucijekoristeći SIRV model, mi predlažemo odgovarajuću estimaciju, uostalom pretpostavljenihparametara cirkularnosti i sfernosti. Integrišući posljednju u test sferne simetrije, formiranje metod za evaluaciju prikladnosti statističkog modela SIRV u kontekstu POLSAR slikavisoke rezolucije. Posmatrajući stopu odbacivanja, sumnja u opravdanost pretpostavke cirkularnostii sfernosti se čini opravdanom, dok pogodnost SIRV modela mora biti preispitana uregionima koje karakteriše jako determinističko elektromagnetno rasijanje. U nastavku, kaoprimarni doprinos ove teze, predlažemo polarimetrijsku nekoherentnu dekompoziciju mete,koja je bazirana na ICA metodu i zasnovana na hipotezi o otklonu od Gausove prirode POLSARclutter -a. Eksploatišući informaciju sadržanu u višim statističkim redovima, predloženadekompozicija daje na izlazu set međusobno nezavisnih (prije nego samo nekorelisanih), neortogonalnihvektora mete. Za razliku od prve dominante komponente, koja je skoro identičnakomponenti estimiranoj koristeći konvencionalnu ICTD metodu, druga dominanta komponentase značajno razlikuje, što predstavlja dodatan potencijal u interpretaciji POLSAR slika.Prvi prezentovani doprinos u primijenjenom kontekstu bio bi stohastički metod za mapiranjesniježnog pokrivača baziran na multitemporalnom setu SAR slika. Najznačajniji priloziprezentovane metode grupi tehnika "detekcije promjene" u mapiranju sniježnog pokrivača suopravdano promijenjena pretpostavka o količniku povratnog rasijanja djelimično otopljenog isuvog snijega, kao i implicitno uvedeno prostorna korelacija između regiona djelimično otopljenogsnijega, postignuta direktnim uvođenjem lokalne statistike speckle šuma u proces odlučivanja.Konačno, predlažemo neautonomni metod za prostornu estimaciju SWE parametra,zasnovan na optičkim slikama. Uspješno inflitrirajući teledetekciju u kalibraciju eksternogSWE modela, imamo za cilj demonstrirati njenu korisnost i neophodnost u monitoringu sniježnogpokrivača.

This thesis comprises two research axes. The first, being rather methodological, consists of our efforts to answer some of the open questions in the POLSAR community, while the latter is sooner related to the specific application - the remote sensing of snow.Following the alternative statistical modelling of highly textured multivariate SAR datasets by means of SIRV model, we propose the appropriate assessment of, otherwise assumed, cir- cularity and sphericity parameters. The last is coupled with the spherical symmetry test, forming a method for the evaluation of SIRV statistical model suitability in the context of POLSAR data. Given the rejection rate, challenging circularity and sphericity appears to be justified, while SIRV model pertinence must be doubted in the regions characterized by strong deterministic scattering. Further on, as the highlight of this thesis, we propose a po- larimetric incoherent target decomposition, based on ICA and founded on the hypothesis of non-Gaussianity of POLSAR clutter. By exploiting the information contained in the higher statistical orders, this decomposition provides at the output a set of mutually independent (rather than only decorrelated), non-orthogonal target vectors. Unlike the first dominant com- ponent, which is nearly identical to the one estimated by the conventional ICTD counterpart, the second dominant component differs significantly, which, as we anticipate, represents an additional potential for the POLSAR datasets interpretation.In the applied context, the first presented contribution would be a stochastic approach in snow mapping by means of multitemporal SAR datasets. The most notable supplements of the presented method to the ensemble of change detection techniques in snow mapping are the plausibly modified assumption of the wet/dry snow backscattering ratio and implicitly introduced spatial correlation between wet snow areas, achieved by directly implicating local speckle statistics in the decision process. Finally, we present the non-autonomous method for SWE spatial estimation, based on optical datasets. By successfully involving the remote sensing datasets in the calibration of the external SWE model, we seek to demonstrate the utility and the necessity of the former in the snow pack monitoring.; Cette thèse est composée de deux axes de recherche. Le premièr, plutôt méthodologique, consiste de nos efforts pour répondre à des questions ouvertes dans la communauté de RSO polarimétrique, tandis que le second est plutôt lié à l'application spécifique - le télédétection du manteau neigeux.Suite à la modélisation statistique alternative des images RSO multivariées et haute- ment texturées, par le modèle SIRV, nous proposons d'abord une évaluation appropriée des paramètres de circularité et sphéricité, autrement à priori présumés. La dernière est accouplée avec le test de symétrie sphérique, ce qui forme une méthode pour l'évaluation de pertinence de modèle statistique SIRV dans le contexte de données RSO polarimétriques. Compte tenu du taux de réjection, cela parait justifié de mettre en question les hypothèses de circularité et sphéricité, alors que la pertinence de modèle SIRV doit être soupçonnée dans les régions caractérisées par la diffusion déterministe forte. En suite, comme le point culminant de cette thèse, nous proposons une décomposition incohérente de cible polarimétrique, basée sur l'ACI et fondée sur l'hypothèse de non-Gaussianité du clutter RSO polarimétrique. En exploitant l'information contenue dans les ordres statistiques supérieurs, cette décomposition donne à la sortie un ensemble de vecteurs de cible, qui sont mutuellement indépendants (plutôt que seule- ment décorrélés) et non-orthogonaux. Contrairement à la première composant dominante, qui parait presque identique à l'une estimée par le homologue conventionnel, la deuxième com- posante dominante diffère significativement, ce qui représente un potentiel additionnel pour l'interprétation des données RSO polarimétriques.Dans le contexte appliqué, la première contribution présentée serait une approche stochas- tique pour la cartographie du manteau neigeux au moyen de données RSO multi-temporelles. Les apports les plus notables de la méthode présentée à l'ensemble de techniques de la détec- tion de changements dans la cartographie du manteau neigeux, sont l'hypothèse modifiée du ratio de rétrodiffusion entre la neige sèche et la neige humide, aussi que la corrélation spatiale entre les régions de la neige humide, introduite en impliquant la statistique locale de speckle dans le processus de décision. Finalement, nous présentons la méthode non-autonome pour l'estimation spatiale de l'équivalent en eau de la neige (EEN), basée sur des données optiques. En utilisant avec succès des données de télédétection dans la calibration du modèle EEN externe, on essaie de démontrer l'utilité et la nécessite du télédétection dans la surveillance du manteau neigeux.; Ova teza podrazumijeva dva pravca istraživanja. Prvi, metodološki, predstavlja naše naporeda odgovorimo na neka od otvorenih pitanja u POLSAR zajednici, dok je drugi vezan zaspecifičnu aplikaciju - teledetekciju sniježnog pokrivača.Uzevši u obzir alternativno statističko modelovanje multivarijantnih SAR slika visoke rezolucijekoristeći SIRV model, mi predlažemo odgovarajuću estimaciju, uostalom pretpostavljenihparametara cirkularnosti i sfernosti. Integrišući posljednju u test sferne simetrije, formiranje metod za evaluaciju prikladnosti statističkog modela SIRV u kontekstu POLSAR slikavisoke rezolucije. Posmatrajući stopu odbacivanja, sumnja u opravdanost pretpostavke cirkularnostii sfernosti se čini opravdanom, dok pogodnost SIRV modela mora biti preispitana uregionima koje karakteriše jako determinističko elektromagnetno rasijanje. U nastavku, kaoprimarni doprinos ove teze, predlažemo polarimetrijsku nekoherentnu dekompoziciju mete,koja je bazirana na ICA metodu i zasnovana na hipotezi o otklonu od Gausove prirode POLSARclutter -a. Eksploatišući informaciju sadržanu u višim statističkim redovima, predloženadekompozicija daje na izlazu set međusobno nezavisnih (prije nego samo nekorelisanih), neortogonalnihvektora mete. Za razliku od prve dominante komponente, koja je skoro identičnakomponenti estimiranoj koristeći konvencionalnu ICTD metodu, druga dominanta komponentase značajno razlikuje, što predstavlja dodatan potencijal u interpretaciji POLSAR slika.Prvi prezentovani doprinos u primijenjenom kontekstu bio bi stohastički metod za mapiranjesniježnog pokrivača baziran na multitemporalnom setu SAR slika. Najznačajniji priloziprezentovane metode grupi tehnika "detekcije promjene" u mapiranju sniježnog pokrivača suopravdano promijenjena pretpostavka o količniku povratnog rasijanja djelimično otopljenog isuvog snijega, kao i implicitno uvedeno prostorna korelacija između regiona djelimično otopljenogsnijega, postignuta direktnim uvođenjem lokalne statistike speckle šuma u proces odlučivanja.Konačno, predlažemo neautonomni metod za prostornu estimaciju SWE parametra,zasnovan na optičkim slikama. Uspješno inflitrirajući teledetekciju u kalibraciju eksternogSWE modela, imamo za cilj demonstrirati njenu korisnost i neophodnost u monitoringu sniježnogpokrivača.

International audience; Blind source separation is one of the major areas of research in signal and image processing today.Being a broad area, it actually comprisesvarious distinctive methods. The revolutionary one, considered here, is Independent Component Analysis (ICA). Nowadays, the FastICA method, based on the fixed-point iterative algorithm, is probably the most widespread tool in estimating independent components. In this paper we analyse the performances of this algorithm when applied on 2D signal (image).

National audience