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Large-scale remote sensing image retrieval by deep hashing neural networks / Yansheng Li in IEEE Transactions on geoscience and remote sensing, vol 56 n° 2 (February 2018)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 56 n° 2 (February 2018) . - pp 950 - 965 Titre : Large-scale remote sensing image retrieval by deep hashing neural networks Type de document : Article/Communication Auteurs : Yansheng Li, Auteur ; Yongjun Zhang, Auteur ; Xin Huang, Auteur ; Hu Zhu, Auteur ; Jiayi Ma, Auteur Année de publication : 2018 Article en page(s) : pp 950 - 965 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal [Termes IGN] données d'entrainement (apprentissage automatique) Résumé : (Auteur) As one of the most challenging tasks of remote sensing big data mining, large-scale remote sensing image retrieval has attracted increasing attention from researchers. Existing large-scale remote sensing image retrieval approaches are generally implemented by using hashing learning methods, which take handcrafted features as inputs and map the high-dimensional feature vector to the low-dimensional binary feature vector to reduce feature-searching complexity levels. As a means of applying the merits of deep learning, this paper proposes a novel large-scale remote sensing image retrieval approach based on deep hashing neural networks (DHNNs). More specifically, DHNNs are composed of deep feature learning neural networks and hashing learning neural networks and can be optimized in an end-to-end manner. Rather than requiring to dedicate expertise and effort to the design of feature descriptors, we can automatically learn good feature extraction operations and feature hashing mapping under the supervision of labeled samples. To broaden the application field, DHNNs are evaluated under two representative remote sensing cases: scarce and sufficient labeled samples. To make up for a lack of labeled samples, DHNNs can be trained via transfer learning for the former case. For the latter case, DHNNs can be trained via supervised learning from scratch with the aid of a vast number of labeled samples. Extensive experiments on one public remote sensing image data set with a limited number of labeled samples and on another public data set with plenty of labeled samples show that the proposed remote sensing image retrieval approach based on DHNNs can remarkably outperform state-of-the-art methods under both of the examined conditions. Numéro de notice : A2018-192 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2017.2756911 Date de publication en ligne : 13/10/2017 En ligne : https://doi.org/10.1109/TGRS.2017.2756911 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89857 [article]