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Variational learning of mixture wishart model for PolSAR image classification / Qian Wu in IEEE Transactions on geoscience and remote sensing, vol 57 n° 1 (January 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 57 n° 1 (January 2019) . - pp 141 - 154 Titre : Variational learning of mixture wishart model for PolSAR image classification Type de document : Article/Communication Auteurs : Qian Wu, Auteur ; Biao Hou, Auteur ; Zaidao Wen, Auteur ; Licheng Jiao, Auteur Année de publication : 2019 Article en page(s) : pp 141 - 154 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] classification [Termes IGN] image AIRSAR [Termes IGN] image radar moirée [Termes IGN] image Radarsat [Termes IGN] loi de Wishart [Termes IGN] optimisation (mathématiques) [Termes IGN] polarimétrie radar Résumé : (Auteur) The phase difference, amplitude product, and amplitude ratio between two polarizations are important discriminators for terrain classification, which derives a significant statistical-distribution-based polarimetric synthetic aperture radar (PolSAR) image classification. Traditionally, statistical-distribution-based PolSAR image classification models pay attention to two aspects: searching for a suitable distribution to model certain PolSAR image and a satisfactory solution for the corresponding distribution model with samples in every terrain. Usually, the described distribution form is too complicated to build. Besides, inaccurate parameter estimation may lead to poor classification performance for PolSAR image. In order to refrain from this phenomenon, a variational thought is adopted for the statistical-distribution-based PolSAR classification method in this paper. First, a mixture Wishart model is built to model the PolSAR image to replace the complicated distribution for the PolSAR image. Second, a learning-based method is suggested instead of inaccurate point estimation of parameters to determine the distribution for every class in the mixture Wishart model. Finally, the proposed learning-based mixture Wishart model will be built as a variational form to realize a parametric model for PolSAR image classification. In the experiments, it will be proved that the class centers are easier to distinguish among different terrains learned from the proposed variational model. In addition, a classification performance on the PolSAR image is superior to the original point estimation Wishart model on both visual classification result and accuracy. Numéro de notice : A2019-104 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2018.2852633 Date de publication en ligne : 16/08/2018 En ligne : https://doi.org/10.1109/TGRS.2018.2852633 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=92410 [article]