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Auteur Zitong Wu |
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Multiscale CNN with autoencoder regularization joint contextual attention network for SAR image classification / Zitong Wu in IEEE Transactions on geoscience and remote sensing, vol 59 n° 2 (February 2021)
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Titre : Multiscale CNN with autoencoder regularization joint contextual attention network for SAR image classification Type de document : Article/Communication Auteurs : Zitong Wu, Auteur ; Biao Hou, Auteur ; Licheng Jiao, Auteur Année de publication : 2021 Article en page(s) : pp 1200 - 1213 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image radar et applications
[Termes IGN] apprentissage profond
[Termes IGN] attention (apprentissage automatique)
[Termes IGN] classification contextuelle
[Termes IGN] classification par réseau neuronal convolutif
[Termes IGN] image radar moiréeRésumé : (auteur) Synthetic aperture radar (SAR) image classification is a fundamental research direction in image interpretation. With the development of various intelligent technologies, deep learning techniques are gradually being applied to SAR image classification. In this study, a new SAR classification algorithm known as the multiscale convolutional neural network with an autoencoder regularization joint contextual attention network (MCAR-CAN) is proposed. The MCAR-CAN has two branches: the autoencoder regularization branch and the context attention branch. First, autoencoder regularization is used for the reconstruction of the input to regularize the classification in the autoencoder regularization branch. Multiscale input and an asymmetric structure of the autoencoder branch cause the network more to be focused on classification than on reconstruction. Second, the attention mechanism is used to produce an attention map in which each attention weight corresponds to a context correlation in attention branch. The robust features are obtained by the attention mechanism. Finally, the features obtained by the two branches are spliced for classification. In addition, a new training strategy and a postprocessing method are designed to further improve the classification accuracy. Experiments performed on the data from three SAR images demonstrated the effectiveness and robustness of the proposed algorithm. Numéro de notice : A2021-113 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3004911 Date de publication en ligne : 07/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3004911 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=96918
in IEEE Transactions on geoscience and remote sensing > vol 59 n° 2 (February 2021) . - pp 1200 - 1213[article]