Nouvelle Méthode en Cascade pour la Classification Hiérarchique Multi-Temporelle ou Multi-Capteur d'Images Satellitaires Haute Résolution

International audienceThis paper describes a method dedicated to multi-resolution, multi-date and eventually multi-sensor classification based on explicit statistical modeling through hierarchical Markov random field modeling based on quad-tree. The proposed approach consists of a supervised Bayesian classifier that combines a joint class-conditional statistical model for pixelwise information and a hierarchical Markov random field for spatio-temporal and multiresolution contextual information fusion based on the Marginal Posterior Mode (MPM). The aim is to recursively maximize the posterior marginal at each pixel, which associates the most probable class label given the entire input information. Within this framework, an interesting novel element of the proposed approach is the use of multiple quadtrees in cascade, each associated with a new image in the available set in order to characterize the correlations associated with distinct images in the data set.Ce papier présente un modèle de classification multi-résolution, multi-date et éventuellement multi-capteur fondé sur une modélisation statistique explicite au travers d'un modèle hiérarchique de champs de Markov construit sur une structure quad-arbre. L'approche proposée consiste en un classifieur bayésien supervisé qui combine un modèle statistique condi-tionnel par classe et un champ de Markov hiérarchique fusionnant l'information spatio-temporelle et multi-résolution. La méthode proposée intégre des informations pixel par pixel à la même résolution. Cela en se basant sur le critère des Modes Marginales a Posteriori (MPM en anglais), qui vise à affecter à chaque pixel l'étiquette optimale en maximisant récursivement la probabilité marginale a posteriori, étant donné l'ensemble des observations multi-temporelles ou multi-capteur. Une des originalités de l'approche proposée est l'utilisation en cascade de plusieurs quad-arbres, chacun étant associé à une nouvelle image disponible, en vue de caractériser les corrélations associées à des images distinctes

Hierarchical Markov random fields for high resolution land cover classification of multisensor and multiresolution image time series

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New hierarchical joint classification method of SAR-optical multiresolution remote sensing data

International audienceIn this paper, we develop a novel classification approach for multiresolution, multisensor (optical and synthetic aperture radar), and/or multiband images. Accurate and time-efficient classification methods are particularly important tools to support rapid and reliable assessment of the ground changes. Given the huge amount and variety of data available currently from last-generation satellite missions , the main difficulty is to develop a classifier that can take benefit of multiband, multiresolution, and multisen-sor input imagery. The proposed method addresses the problem of multisensor fusion of SAR with optical data for classification purposes, and allows input data collected at multiple resolutions and additional multiscale features derived through wavelets to be fused

Modèles de classification hiérarchiques d'images satellitaires multi-résolutions, multi-temporelles et multi-capteurs. Application aux désastres naturels

The capabilities to monitor the Earth's surface, notably in urban and built-up areas, for example in the framework of the protection from environmental disasters such as floods or earthquakes, play important roles in multiple social, economic, and human viewpoints. In this framework, accurate and time-efficient classification methods are important tools required to support the rapid and reliable assessment of ground changes and damages induced by a disaster, in particular when an extensive area has been affected. Given the substantial amount and variety of data available currently from last generation very-high resolution (VHR) satellite missions such as Pléiades, COSMO-SkyMed, or RadarSat-2, the main methodological difficulty is to develop classifiers that are powerful and flexible enough to utilize the benefits of multiband, multiresolution, multi-date, and possibly multi-sensor input imagery. With the proposed approaches, multi-date/multi-sensor and multi-resolution fusion are based on explicit statistical modeling. The method combines a joint statistical model of multi-sensor and multi-temporal images through hierarchical Markov random field (MRF) modeling, leading to statistical supervised classification approaches. We have developed novel hierarchical Markov random field models, based on the marginal posterior modes (MPM) criterion, that support information extraction from multi-temporal and/or multi-sensor information and allow the joint supervised classification of multiple images taken over the same area at different times, from different sensors, and/or at different spatial resolutions. The developed methods have been experimentally validated with complex optical multispectral (Pléiades), X-band SAR (COSMO-Skymed), and C-band SAR (RadarSat-2) imagery taken from the Haiti siteLes moyens mis en œuvre pour surveiller la surface de la Terre, notamment les zones urbaines, en cas de catastrophes naturelles telles que les inondations ou les tremblements de terre, et pour évaluer l’impact de ces événements, jouent un rôle primordial du point de vue sociétal, économique et humain. Dans ce cadre, des méthodes de classification précises et efficaces sont des outils particulièrement importants pour aider à l’évaluation rapide et fiable des changements au sol et des dommages provoqués. Étant données l’énorme quantité et la variété des données Haute Résolution (HR) disponibles grâce aux missions satellitaires de dernière génération et de différents types, telles que Pléiades, COSMO-SkyMed ou RadarSat-2 la principale difficulté est de trouver un classifieur qui puisse prendre en compte des données multi-bande, multi-résolution, multi-date et éventuellement multi-capteur tout en gardant un temps de calcul acceptable. Les approches de classification multi-date/multi-capteur et multi-résolution sont fondées sur une modélisation statistique explicite. En fait, le modèle développé consiste en un classifieur bayésien supervisé qui combine un modèle statistique conditionnel par classe intégrant des informations pixel par pixel à la même résolution et un champ de Markov hiérarchique fusionnant l’information spatio-temporelle et multi-résolution, en se basant sur le critère des Modes Marginales a Posteriori (MPM en anglais), qui vise à affecter à chaque pixel l’étiquette optimale en maximisant récursivement la probabilité marginale a posteriori, étant donné l’ensemble des observations multi-temporelles ou multi-capteu

Hierarchical joint classification models for multi-resolution, multi-temporal and multi-sensor remote sensing images. Application to natural disasters

Les moyens mis en œuvre pour surveiller la surface de la Terre, notamment les zones urbaines, en cas de catastrophes naturelles telles que les inondations ou les tremblements de terre, et pour évaluer l’impact de ces événements, jouent un rôle primordial du point de vue sociétal, économique et humain. Dans ce cadre, des méthodes de classification précises et efficaces sont des outils particulièrement importants pour aider à l’évaluation rapide et fiable des changements au sol et des dommages provoqués. Étant données l’énorme quantité et la variété des données Haute Résolution (HR) disponibles grâce aux missions satellitaires de dernière génération et de différents types, telles que Pléiades, COSMO-SkyMed ou RadarSat-2 la principale difficulté est de trouver un classifieur qui puisse prendre en compte des données multi-bande, multi-résolution, multi-date et éventuellement multi-capteur tout en gardant un temps de calcul acceptable. Les approches de classification multi-date/multi-capteur et multi-résolution sont fondées sur une modélisation statistique explicite. En fait, le modèle développé consiste en un classifieur bayésien supervisé qui combine un modèle statistique conditionnel par classe intégrant des informations pixel par pixel à la même résolution et un champ de Markov hiérarchique fusionnant l’information spatio-temporelle et multi-résolution, en se basant sur le critère des Modes Marginales a Posteriori (MPM en anglais), qui vise à affecter à chaque pixel l’étiquette optimale en maximisant récursivement la probabilité marginale a posteriori, étant donné l’ensemble des observations multi-temporelles ou multi-capteurThe capabilities to monitor the Earth's surface, notably in urban and built-up areas, for example in the framework of the protection from environmental disasters such as floods or earthquakes, play important roles in multiple social, economic, and human viewpoints. In this framework, accurate and time-efficient classification methods are important tools required to support the rapid and reliable assessment of ground changes and damages induced by a disaster, in particular when an extensive area has been affected. Given the substantial amount and variety of data available currently from last generation very-high resolution (VHR) satellite missions such as Pléiades, COSMO-SkyMed, or RadarSat-2, the main methodological difficulty is to develop classifiers that are powerful and flexible enough to utilize the benefits of multiband, multiresolution, multi-date, and possibly multi-sensor input imagery. With the proposed approaches, multi-date/multi-sensor and multi-resolution fusion are based on explicit statistical modeling. The method combines a joint statistical model of multi-sensor and multi-temporal images through hierarchical Markov random field (MRF) modeling, leading to statistical supervised classification approaches. We have developed novel hierarchical Markov random field models, based on the marginal posterior modes (MPM) criterion, that support information extraction from multi-temporal and/or multi-sensor information and allow the joint supervised classification of multiple images taken over the same area at different times, from different sensors, and/or at different spatial resolutions. The developed methods have been experimentally validated with complex optical multispectral (Pléiades), X-band SAR (COSMO-Skymed), and C-band SAR (RadarSat-2) imagery taken from the Haiti sit

Classification of Multisensor and Multiresolution Remote Sensing Images Through Hierarchical Markov Random Fields

reserved4siThis letter proposes two methods for the supervised classification of multisensor optical and synthetic aperture radar images with possibly different spatial resolutions. Both the methods are formulated within a unique framework based on hierarchical Markov random fields. Distinct quad-trees associated with the individual information sources are defined to jointly address multisensor, multiresolution, and possibly multifrequency fusion, and are integrated with finite mixture models and the marginal posterior mode criterion. Experimental validation is conducted with Pléiades, COSMO-SkyMed, RADARSAT-2, and GeoEye-1 data.mixedHedhli, Ihsen; Moser, Gabriele*; Serpico, Sebastiano B.; Zerubia, JosianeHedhli, Ihsen; Moser, Gabriele; Serpico, Sebastiano B.; Zerubia, Josian

Fusion of multitemporal and multiresolution remote sensing data and application to natural disasters

International audienceIn this paper, we propose a novel method to fuse multidate, multiresolution, and multiband remote sensing imagery for multitemporal classification purposes. The proposed method is based on an explicit hierarchical graph-based model that is sufficiently flexible to deal with multisource coregistered time series of images collected at different spatial resolutions. An especially novel element of the proposed approach is the use of multiple quad-trees in cascade, each associated with an image acquired at a different date, with the aim to characterize the temporal correlations associated with distinct images in an input time series. Experimental results are shown with multitemporal and multiresolution Pléiades data

New cascade model for hierarchical joint classification of multisensor and multiresolution remote sensing data

This paper addresses the problem of multisensor fusion of COSMO-SkyMed and RADARSAT-2 data together with optical imagery for classification purposes. The proposed method is based on an explicit hierarchical graph-based model that is sufficiently flexible to deal with multisource coregistered images collected at different spatial resolutions by different sensors. An especially novel element of the proposed approach is the use of multiple quad-trees in cascade, each associated with a set of images acquired by different SAR sensors, with the aim to characterize the correlations associated with distinct images from different instruments. Experimental results are shown with COSMO-SkyMed, RADARSAT-2, and Pl\ue9iades data1