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Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification / Rama Rao Nidamanuri in ISPRS Journal of photogrammetry and remote sensing, vol 138 (April 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 138 (April 2018) . - pp 74 - 85 Titre : Binary patterns encoded convolutional neural networks for texture recognition and remote sensing scene classification Type de document : Article/Communication Auteurs : Rama Rao Nidamanuri, Auteur ; Fahad Shahbaz Khan, Auteur ; Joost van de Weijer, Auteur ; Matthieu Molinier, Auteur ; Jorma Laaksonen, Auteur Année de publication : 2018 Article en page(s) : pp 74 - 85 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse texturale [Termes IGN] apprentissage profond [Termes IGN] classification [Termes IGN] image RVB [Termes IGN] motif binaire local [Termes IGN] réseau neuronal convolutif [Termes IGN] texture d'image Résumé : (Auteur) Designing discriminative powerful texture features robust to realistic imaging conditions is a challenging computer vision problem with many applications, including material recognition and analysis of satellite or aerial imagery. In the past, most texture description approaches were based on dense orderless statistical distribution of local features. However, most recent approaches to texture recognition and remote sensing scene classification are based on Convolutional Neural Networks (CNNs). The de facto practice when learning these CNN models is to use RGB patches as input with training performed on large amounts of labeled data (ImageNet). In this paper, we show that Local Binary Patterns (LBP) encoded CNN models, codenamed TEX-Nets, trained using mapped coded images with explicit LBP based texture information provide complementary information to the standard RGB deep models. Additionally, two deep architectures, namely early and late fusion, are investigated to combine the texture and color information. To the best of our knowledge, we are the first to investigate Binary Patterns encoded CNNs and different deep network fusion architectures for texture recognition and remote sensing scene classification. We perform comprehensive experiments on four texture recognition datasets and four remote sensing scene classification benchmarks: UC-Merced with 21 scene categories, WHU-RS19 with 19 scene classes, RSSCN7 with 7 categories and the recently introduced large scale aerial image dataset (AID) with 30 aerial scene types. We demonstrate that TEX-Nets provide complementary information to standard RGB deep model of the same network architecture. Our late fusion TEX-Net architecture always improves the overall performance compared to the standard RGB network on both recognition problems. Furthermore, our final combination leads to consistent improvement over the state-of-the-art for remote sensing scene classification. Numéro de notice : A2018-121 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2018.01.023 Date de publication en ligne : 15/02/2018 En ligne : https://doi.org/10.1016/j.isprsjprs.2018.01.023 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=89590 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018041	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018043	DEP-EXM	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2018042	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt

Detecting man-made structures and changes in satellite imagery with a content-based information retrieval system built on Self-Organizing Maps / Matthieu Molinier in IEEE Transactions on geoscience and remote sensing, vol 45 n° 4 (April 2007)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > vol 45 n° 4 (April 2007) . - pp 861 - 874 Titre : Detecting man-made structures and changes in satellite imagery with a content-based information retrieval system built on Self-Organizing Maps Type de document : Article/Communication Auteurs : Matthieu Molinier, Auteur ; Jorma Laaksonen, Auteur ; Tuomas Häme, Auteur Année de publication : 2007 Article en page(s) : pp 861 - 874 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Bases de données localisées [Termes IGN] base de données d'images [Termes IGN] carte de Kohonen [Termes IGN] détection de changement [Termes IGN] détection du bâti [Termes IGN] image à très haute résolution [Termes IGN] image optique [Termes IGN] image satellite [Termes IGN] recherche d'information Résumé : (Auteur) The increasing amount and resolution of satellite sensors demand new techniques for browsing remote sensing image archives. Content-based querying allows an efficient retrieval of images based on the information they contain, rather than their acquisition date or geographical extent. Self-organizing maps (SOMs) have been successfully applied in the PicSOM system to content-based image retrieval in databases of conventional images. In this paper, we investigate and extend the potential of PicSOM for the analysis of remote sensing data. We propose methods for detecting man-made structures, as well as supervised and unsupervised change detection, based on the same framework. In this paper, a database was artificially created by splitting each satellite image to be analyzed into small images. After training the PicSOM on this imagelet database, both interactive and off-line queries were made to detect man-made structures, as well as changes between two very high resolution images from different years. Experimental results were both evaluated quantitatively and discussed qualitatively, and suggest that this new approach is suitable for analyzing very high resolution optical satellite imagery. Possible applications of this work include interactive detection of man-made structures or supervised monitoring of sensitive sites. Copyright IEEE Numéro de notice : A2007-220 Affiliation des auteurs : non IGN Thématique : GEOMATIQUE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2006.890580 En ligne : https://doi.org/10.1109/TGRS.2006.890580 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=28583 [article]

Exemplaires(1)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
065-07041	RAB	Revue	Centre de documentation	En réserve L003	Disponible