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Object-based fusion of multitemporal multiangle ENVISAT ASAR and HJ-1B multispectral data for urban land-cover mapping / Yifang Ban in IEEE Transactions on geoscience and remote sensing, vol 51 n° 4 Tome 1 (April 2013)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 51 n° 4 Tome 1 (April 2013) . - pp 1998 - 2006 Titre : Object-based fusion of multitemporal multiangle ENVISAT ASAR and HJ-1B multispectral data for urban land-cover mapping Type de document : Article/Communication Auteurs : Yifang Ban, Auteur ; Alexender Jacob, Auteur Année de publication : 2013 Article en page(s) : pp 1998 - 2006 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] carte d'occupation du sol [Termes IGN] conflation [Termes IGN] fusion de données multisource [Termes IGN] image Envisat-ASAR [Termes IGN] image HJ-1B [Termes IGN] image multibande [Termes IGN] image multitemporelle [Termes IGN] image radar moirée [Termes IGN] Pékin (Chine) [Termes IGN] segmentation d'image [Termes IGN] zone urbaine Résumé : (Auteur) The objectives of this research are to develop robust methods for segmentation of multitemporal synthetic aperture radar (SAR) and optical data and to investigate the fusion of multitemporal ENVISAT advanced synthetic aperture radar (ASAR) and Chinese HJ-1B multispectral data for detailed urban land-cover mapping. Eight-date multiangle ENVISAT ASAR images and one-date HJ-1B charge-coupled device image acquired over Beijing in 2009 are selected for this research. The edge-aware region growing and merging (EARGM) algorithm is developed for segmentation of SAR and optical data. Edge detection using a Sobel filter is applied on SAR and optical data individually, and a majority voting approach is used to integrate all edge images. The edges are then used in a segmentation process to ensure that segments do not grow over edges. The segmentation is influenced by minimum and maximum segment sizes as well as the two homogeneity criteria, namely, a measure of color and a measure of texture. The classification is performed using support vector machines. The results show that our EARGM algorithm produces better segmentation than eCognition, particularly for built-up classes and linear features. The best classification result (80%) is achieved using the fusion of eight-date ENVISAT ASAR and HJ-1B data. This represents 5%, 11%, and 14% improvements over eCognition, HJ-1B, and ASAR classifications, respectively. The second best classification is achieved using fusion of four-date ENVISAT ASAR and HJ-1B data (78%). The result indicates that fewer multitemporal SAR images can achieve similar classification accuracy if multitemporal multiangle dual-look-direction SAR data are carefully selected. Numéro de notice : A2013-213 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2012.2236560 En ligne : https://doi.org/10.1109/TGRS.2012.2236560 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32351 [article]