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Rotation-invariant feature learning in VHR optical remote sensing images via nested siamese structure with double center loss / Ruoqiao Jiang in IEEE Transactions on geoscience and remote sensing, vol 59 n° 4 (April 2021)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 59 n° 4 (April 2021) . - pp 3326 - 3337 Titre : Rotation-invariant feature learning in VHR optical remote sensing images via nested siamese structure with double center loss Type de document : Article/Communication Auteurs : Ruoqiao Jiang, Auteur ; Shaohui Mei, Auteur ; Mingyang Ma, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : pp 3326 - 3337 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection d'objet [Termes IGN] données d'entrainement (apprentissage automatique) [Termes IGN] échantillon [Termes IGN] extraction de traits caractéristiques [Termes IGN] image à très haute résolution [Termes IGN] invariant [Termes IGN] réseau neuronal siamois [Termes IGN] rotation Résumé : (auteur) Rotation-invariant features are of great importance for object detection and image classification in very-high-resolution (VHR) optical remote sensing images. Though multibranch convolutional neural network (mCNN) has been demonstrated to be very effective for rotation-invariant feature learning, how to effectively train such a network is still an open problem. In this article, a nested Siamese structure (NSS) is proposed for training the mCNN to learn effective rotation-invariant features, which consists of an inner Siamese structure to enhance intraclass cohesion and an outer Siamese structure to enlarge interclass margin. Moreover, a double center loss (DCL) function, in which training samples from the same class are mapped closer to each other while those from different classes are mapped far away to each other, is proposed to train the proposed NSS even with a small amount of training samples. Experimental results over three benchmark data sets demonstrate that the proposed NSS trained by DCL is very effective to encounter rotation varieties when learning features for image classification and outperforms several state-of-the-art rotation-invariant feature learning algorithms even when a small amount of training samples are available. Numéro de notice : A2021-286 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2020.3021283 Date de publication en ligne : 18/07/2020 En ligne : https://doi.org/10.1109/TGRS.2020.3021283 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97395 [article]