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Multilayer projective dictionary pair learning and sparse autoencoder for PolSAR image classification / Yanqiao Chen in IEEE Transactions on geoscience and remote sensing, vol 55 n° 12 (December 2017)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 55 n° 12 (December 2017) . - pp 6683 - 6694 Titre : Multilayer projective dictionary pair learning and sparse autoencoder for PolSAR image classification Type de document : Article/Communication Auteurs : Yanqiao Chen, Auteur ; Licheng Jiao, Auteur ; Yangyang Li, Auteur ; Jin Zhao, Auteur Année de publication : 2017 Article en page(s) : pp 6683 - 6694 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications [Termes IGN] apprentissage dirigé [Termes IGN] extraction de traits caractéristiques [Termes IGN] image radar moirée [Termes IGN] Perceptron multicouche [Termes IGN] polarimétrie radar Résumé : (Auteur) Polarimetric synthetic aperture radar (PolSAR) image classification is a vital application in remote sensing image processing. In general, PolSAR image classification is actually a high-dimensional nonlinear mapping problem. The methods based on sparse representation and deep learning have shown a great potential for PolSAR image classification. Therefore, a novel PolSAR image classification method based on multilayer projective dictionary pair learning (MDPL) and sparse auto encoder (SAE) is proposed in this paper. First, MDPL is used to extract features, and the abstract degree of the extracted features is high. Second, in order to get the nonlinear relationship between elements of feature vectors in an adaptive way, SAE is also used in this paper. Three PolSAR images are used to test the effectiveness of our method. Compared with several state-of-the-art methods, our method achieves very competitive results in PolSAR image classification. Numéro de notice : A2017-764 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2727067 En ligne : https://doi.org/10.1109/TGRS.2017.2727067 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=88800 [article]