146 research outputs found
Change detection in multisensor SAR images using bivariate gamma distributions
This paper studies a family of distributions constructed from multivariate gamma distributions to model the statistical properties of multisensor synthetic aperture radar (SAR) images. These distributions referred to as multisensor multivariate gamma distributions (MuMGDs) are potentially interesting for detecting changes in SAR images acquired by different sensors having different numbers of looks. The first part of the paper compares different estimators for the parameters of MuMGDs. These estimators are based on the maximum likelihood principle, the method of inference function for margins and the method of moments. The second part of the paper studies change detection algorithms based on the estimated correlation coefficient of MuMGDs. Simulation results conducted on synthetic and real data illustrate the performance of these change detectors
On the possibility of automatic multisensor image registration
International audienceMultisensor image registration is needed in a large number of applications of remote sensing imagery. The accuracy achieved with usual methods (manual control points extraction, estimation of an analytical deformation model) is not satisfactory for many applications where a subpixel accuracy for each pixel of the image is needed (change detection or image fusion, for instance). Unfortunately, there are few works in the literature about the fine registration of multisensor images and even less about the extension of approaches similar to those based on fine correlation for the case of monomodal imagery. In this paper, we analyze the problem of the automatic multisensor image registration and we introduce similarity measures which can replace the correlation coefficient in a deformation map estimation scheme. We show an example where the deformation map between a radar image and an optical one is fully automatically estimated
Bivariate Gamma Distributions for Image Registration and Change Detection
This paper evaluates the potential interest of using bivariate gamma distributions for image registration and change detection. The first part of this paper studies estimators for the parameters of bivariate gamma distributions based on the maximum likelihood principle and the method of moments. The performance of both methods are compared in terms of estimated mean square errors and theoretical asymptotic variances. The mutual information is a classical similarity measure which can be used for image registration or change detection. The second part of the paper studies some properties of the mutual information for bivariate Gamma distributions. Image registration and change detection techniques based on bivariate gamma distributions are finally investigated. Simulation results conducted on synthetic and real data are very encouraging. Bivariate gamma distributions are good candidates allowing us to develop new image registration algorithms and new change detectors
High-resolution optical and SAR image fusion for building database updating
This paper addresses the issue of cartographic database (DB) creation or updating using high-resolution synthetic aperture radar and optical images. In cartographic applications, objects of interest are mainly buildings and roads. This paper proposes a processing chain to create or update building DBs. The approach is composed of two steps. First, if a DB is available, the presence of each DB object is checked in the images. Then, we verify if objects coming from an image segmentation should be included in the DB. To do those two steps, relevant features are extracted from images in the neighborhood of the considered object. The object removal/inclusion in the DB is based on a score obtained by the fusion of features in the framework of DempsterâShafer evidence theory
LAND-COVER MAPS FROM PARTIALLY CLOUDY MULTI-TEMPORAL IMAGE SERIES: OPTIMAL TEMPORAL SAMPLING AND CLOUD REMOVAL
Utilisation des sĂ©ries temporelles dâimages Sentinel-2 pour la cartographie de lâoccupation du sol dans un contexte de modĂ©lisation de la biodiversitĂ©
La connaissance de lâoccupation du sol actualisĂ©e est une donnĂ©e essentielle pour de nombreuses applications
scientifiques et opĂ©rationnelles. Ă ce titre, il sâagit dâune donnĂ©e permettant de dĂ©river plusieurs variables
essentielles de biodiversitĂ©, telles que lâĂ©tendue et la fragmentation des Ă©cosystĂšmes ainsi que la structure
paysagĂšre, variables fortement reliĂ©es au potentiel de biodiversitĂ© dâun paysage (Skidmore et al., 2015). Elle
reprĂ©sente une donnĂ©e dâentrĂ©e essentielle des modĂšles prĂ©dictifs ou de simulation paysagĂšre dĂ©veloppĂ©es en
recherche en Ă©cologie du paysage. Ă lâheure actuelle, il existe plusieurs jeux de donnĂ©es dâoccupation du sol de
rĂ©fĂ©rence, comme Corine Land Cover (CLC) Ă lâĂ©chelle europĂ©enne ou la BD TOPOÂź de lâIGN Ă lâĂ©chelle
nationale française. Ces deux jeux de donnĂ©es permettent de dĂ©crire lâoccupation du sol de maniĂšre exhaustive
et harmonisée sur de larges étendues géographiques. Cependant, la faiblesse de CLC réside dans sa fraßcheur
temporelle, Ă savoir que sa diffusion intervient tardivement par rapport Ă la pĂ©riode temporelle quâelle dĂ©crit. Si
CLC dispose dâune typologie trĂšs dĂ©taillĂ©e, intĂ©grant des notions dâusage du sol, la BD TOPOÂź, si elle dĂ©crit
prĂ©cisĂ©ment les Ă©lĂ©ments permanents du paysage, nâidentifie pas diffĂ©rentes classes annuelles du paysage
telles que les cultures. LâavĂšnement rĂ©cent de la mission spatiale Sentinelle-2 qui fournit de sĂ©ries temporelles
dâimages satellites, Ă forte capacitĂ© de revisite (5 jours) et une rĂ©solution spatiale dĂ©camĂ©trique sur lâensemble
de la surface terrestre, ouvre ainsi de nouvelles opportunitĂ©s dans la cartographie de lâoccupation du sol
actualisĂ©e Ă grande Ă©chelle. Dans ce sens, le CESBIO avec des contributions de lâUMR Dynafor, dans le cadre
du centre dâexpertise scientifique « Occupation du Sol » (CES OSO) du PĂŽle ThĂ©matique Surfaces
Continentales THEIA a dĂ©veloppĂ© une chaĂźne opĂ©rationnelle de classification supervisĂ©e automatique dâimages
Sentinelle-2 et Landsat-8 (iota2) produisant une cartographie de lâoccupation du sol actualisĂ©e. Lâoccupation du
sol est dĂ©crite grĂące Ă 17 classes, couvrant les grands ensembles paysagers (urbain, agricole et semi-naturel), Ă
une résolution spatiale de 10 m et une unité minimale de collecte de 0.01 ha (UMC). La précision globale proche
de 90% permet son utilisation tant dans des contextes opĂ©rationnels et scientifique dâaide Ă la dĂ©cision (Inglada
et al., 2017). Cette présentation décrira, dans une premiÚre partie, les caractéristiques de ce produit
cartographique, de sa méthode de production et de sa qualité statistique. Dans une seconde partie, la question
de lâincertitude spatiale de cette carte dâoccupation du sol sera abordĂ©e. Une comparaison avec un jeu de
donnĂ©es dâoccupation du sol digitalisĂ© manuellement sera prĂ©sentĂ©e au travers dâune modĂ©lisation spatialisĂ©e
espĂšce-habitat fondĂ©e sur la surface, lâhĂ©tĂ©rogĂ©nĂ©itĂ© et la connectivitĂ© forestiĂšre dâun paysage agricole pour
expliquer la richesse spĂ©cifique de syrphes (ordre des mouches) (Herrault et al., 2016). Les rĂ©sultats tendent Ă
montrer un effet nĂ©gligeable de lâincertitude spatiale sur les performances du modĂšle alors quâen parallĂšle le
recours Ă la cartographie par tĂ©lĂ©dĂ©tection de lâoccupation du sol permet dâenvisager une analyse plus
systĂ©matique de lâeffet de la matrice paysagĂšre dans son ensemble sur la biodiversitĂ©
Comparison of optical sensors discrimination ability using spectral libraries
In remote sensing, the ability to discriminate different land covers or material types is directly linked with the spectral resolution and sampling provided by the optical sensor. Previous studies showed that the spectral resolution is a critical issue, especially in complex environment. In spite of the increasing availability of hyperspectral data, multispectral optical sensors onboard various satellites are acquiring everyday a massive amount of data with a relatively poor spectral resolution (i.e. usually about 4 to 7 spectral bands). These remotely sensed data are intensively used for Earth observation regardless of their limited spectral resolution. In this paper, we studied seven of these optical sensors: Pleiades, QuickBird, SPOT5, Ikonos, Landsat TM, Formosat and Meris. This study focuses on the ability of each sensor to discriminate different materials according to its spectral resolution. We used four different spectral libraries which contains around 2500 spectra of materials and land covers with a fine spectral resolution. These spectra were convolved with the Relative Spectral Responses (RSR) of each sensor to create spectra at the sensorsâ resolutions. Then, these reduced spectra were compared using separability indexes (Divergence, Transformed divergence, Bhattacharyya, Jeffreys-Matusita) and machine learning tools. In the experiments, we highlighted that the spectral bands configuration could lead to important differences in classification accuracy according to the context of application (e.g. urban area)
The Earth Observation Data for Habitat Monitoring (EODHaM) system
To support decisions relating to the use and conservation of protected areas and surrounds, the EU-funded BIOdiversity multi-SOurce monitoring System: from Space TO Species (BIO_SOS) project has developed the Earth Observation Data for HAbitat Monitoring (EODHaM) system for consistent mapping and monitoring of biodiversity. The EODHaM approach has adopted the Food and Agriculture Organization Land Cover Classification System (LCCS) taxonomy and translates mapped classes to General Habitat Categories (GHCs) from which Annex I habitats (EU Habitats Directive) can be defined. The EODHaM system uses a combination of pixel and object-based procedures. The 1st and 2nd stages use earth observation (EO) data alone with expert knowledge to generate classes according to the LCCS taxonomy (Levels 1 to 3 and beyond). The 3rd stage translates the final LCCS classes into GHCs from which Annex I habitat type maps are derived. An additional module quantifies changes in the LCCS classes and their components, indices derived from earth observation, object sizes and dimensions and the translated habitat maps (i.e., GHCs or Annex I). Examples are provided of the application of EODHaM system elements to protected sites and their surrounds in Italy, Wales (UK), the Netherlands, Greece, Portugal and India
Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2
Developing better agricultural monitoring capabilities based on Earth Observation data is critical for strengthening food production information and market transparency. The Sentinel-2 mission has the optimal capacity for regional to global agriculture monitoring in terms of resolution (10â20 meter), revisit frequency (five days) and coverage (global). In this context, the European Space Agency launched in 2014 the âSentinelÂ2 for Agricultureâ project, which aims to prepare the exploitation of Sentinel-2 data for agriculture monitoring through the development of open source processing chains for relevant products. The project generated an unprecedented data set, made of âSentinel-2 likeâ time series and in situ data acquired in 2013 over 12 globally distributed sites. Earth Observation time series were mostly built on the SPOT4 (Take 5) data set, which was specifically designed to simulate Sentinel-2. They also included Landsat 8 and RapidEye imagery as complementary data sources. Images were pre-processed to Level 2A and the quality of the resulting time series was assessed. In situ data about cropland, crop type and biophysical variables were shared by site managers, most of them belonging to the âJoint Experiment for Crop Assessment and Monitoringâ network. This data set allowed testing and comparing across sites the methodologies that will be at the core of the future âSentinelÂ2 for Agricultureâ system.Instituto de Clima y AguaFil: Bontemps, Sophie. UniversitĂ© Catholique de Louvain. Earth and Life Institute; BĂ©lgicaFil: Arias, Marcela. Universite de Toulose - Le Mirail. Centre dâEtudes Spatiales de la BIOsphĂšre; FranciaFil: Cara, Cosmin. CS Romania S.A.; RumaniaFil: Dedieu, GĂ©rard. Universite de Toulose - Le Mirail. Centre dâEtudes Spatiales de la BIOsphĂšre; FranciaFil: Guzzonato, Eric. CS SystĂšmes dâInformation; FranciaFil: Hagolle, Olivier. Universite de Toulose - Le Mirail. Centre dâEtudes Spatiales de la BIOsphĂšre; FranciaFil: Inglada, Jordi. Universite de Toulose - Le Mirail. Centre dâEtudes Spatiales de la BIOsphĂšre; FranciaFil: Matton, Nicolas. UniversitĂ© Catholique de Louvain. Earth and Life Institute; BĂ©lgicaFil: Morin, David. Universite de Toulose - Le Mirail. Centre dâEtudes Spatiales de la BIOsphĂšre; FranciaFil: Popescu, Ramona. CS Romania S.A.; RumaniaFil: Rabaute, Thierry. CS SystĂšmes dâInformation; FranciaFil: Savinaud, Mickael. CS SystĂšmes dâInformation; FranciaFil: Sepulcre, Guadalupe. UniversitĂ© Catholique de Louvain. Earth and Life Institute; BĂ©lgicaFil: Valero, Silvia. Universite de Toulose - Le Mirail. Centre dâEtudes Spatiales de la BIOsphĂšre; FranciaFil: Ahmad, Ijaz. Pakistan Space and Upper Atmosphere Research Commission. Space Applications Research Complex. National Agriculture Information Center Directorate; PakistĂĄnFil: BĂ©guĂ©, AgnĂšs. Centre de CoopĂ©ration Internationale en Recherche Agronomique pour le DĂ©velopperment; FranciaFil: Wu, Bingfang. Chinese Academy of Sciences. Institute of Remote Sensing and Digital Earth; RepĂșblica de ChinaFil: De Abelleyra, Diego. Instituto Nacional de TecnologĂa Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Diarra, Alhousseine. UniversitĂ© Cadi Ayyad. FacultĂ© des Sciences Semlalia; MarruecosFil: Dupuy, StĂ©phane. Centre de CoopĂ©ration Internationale en Recherche Agronomique pour le DĂ©velopperment; FranciaFil: French, Andrew. United States Department of Agriculture. Agricultural Research Service. Arid Land Agricultural Research Center; ArgentinaFil: Akhtar, Ibrar ul Hassan. Pakistan Space and Upper Atmosphere Research Commission. Space Applications Research Complex. National Agriculture Information Center Directorate; PakistĂĄnFil: Kussul, Nataliia. National Academy of Sciences of Ukraine. Space Research Institute and State Space Agency of Ukraine; UcraniaFil: Lebourgeois, Valentine. Centre de CoopĂ©ration Internationale en Recherche Agronomique pour le DĂ©velopperment; FranciaFil: Le Page, Michel. UniversitĂ© Cadi Ayyad. FacultĂ© des Sciences Semlalia. Laboratoire Mixte International TREMA; Marruecos. Universite de Toulose - Le Mirail. Centre dâEtudes Spatiales de la BIOsphĂšre; FranciaFil: Newby, Terrence. Agricultural Research Council; SudĂĄfricaFil: Savin, Igor. V.V. Dokuchaev Soil Science Institute; RusiaFil: VerĂłn, Santiago RamĂłn. Instituto Nacional de TecnologĂa Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Koetz, Benjamin. European Space Agency. European Space Research Institute; ItaliaFil: Defourny, Pierre. UniversitĂ© Catholique de Louvain. Earth and Life Institute; BĂ©lgic
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