Centre de documentation
scientifique

Semantic labeling in very high resolution images via a self-cascaded convolutional neural network / Yoncheng Liu in ISPRS Journal of photogrammetry and remote sensing, vol 145 - part A (November 2018)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 145 - part A (November 2018) . - pp 78 - 95 Titre : Semantic labeling in very high resolution images via a self-cascaded convolutional neural network Type de document : Article/Communication Auteurs : Yoncheng Liu, Auteur ; Bin Fan, Auteur ; Lingfeng Wang, Auteur ; Jun Bai, Auteur ; Shiming Xiang, Auteur ; Chunhong Pan, Auteur Année de publication : 2018 Article en page(s) : pp 78 - 95 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image [Termes IGN] apprentissage profond [Termes IGN] image à très haute résolution [Termes IGN] réseau neuronal convolutif [Termes IGN] zone urbaine Résumé : (Auteur) Semantic labeling for very high resolution (VHR) images in urban areas, is of significant importance in a wide range of remote sensing applications. However, many confusing manmade objects and intricate fine-structured objects make it very difficult to obtain both coherent and accurate labeling results. For this challenging task, we propose a novel deep model with convolutional neural networks (CNNs), i.e., an end-to-end self-cascaded network (ScasNet). Specifically, for confusing manmade objects, ScasNet improves the labeling coherence with sequential global-to-local contexts aggregation. Technically, multi-scale contexts are captured on the output of a CNN encoder, and then they are successively aggregated in a self-cascaded manner. Meanwhile, for fine-structured objects, ScasNet boosts the labeling accuracy with a coarse-to-fine refinement strategy. It progressively refines the target objects using the low-level features learned by CNN’s shallow layers. In addition, to correct the latent fitting residual caused by multi-feature fusion inside ScasNet, a dedicated residual correction scheme is proposed. It greatly improves the effectiveness of ScasNet. Extensive experimental results on three public datasets, including two challenging benchmarks, show that ScasNet achieves the state-of-the-art performance. Numéro de notice : A2018-490 Affiliation des auteurs : non IGN Thématique : IMAGERIE/INFORMATIQUE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1016/j.isprsjprs.2017.12.007 Date de publication en ligne : 21/12/2017 En ligne : https://doi.org/10.1016/j.isprsjprs.2017.12.007 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=91226 [article]

Exemplaires(3)

Code-barres	Cote	Support	Localisation	Section	Disponibilité
081-2018111	RAB	Revue	Centre de documentation	En réserve L003	Disponible
081-2018113	DEP-EXM	Revue	LASTIG	Dépôt en unité	Exclu du prêt
081-2018112	DEP-EAF	Revue	Nancy	Dépôt en unité	Exclu du prêt