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Accurate detection of built-up areas from high-resolution remote sensing imagery using a fully convolutional network / Yihua Tan in Photogrammetric Engineering & Remote Sensing, PERS, vol 85 n° 10 (October 2019)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 85 n° 10 (October 2019) . - pp 737 - 752 Titre : Accurate detection of built-up areas from high-resolution remote sensing imagery using a fully convolutional network Type de document : Article/Communication Auteurs : Yihua Tan, Auteur ; Shengzhou Xiong, Auteur ; Zhi Li, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 737 - 752 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection du bâti [Termes IGN] extraction automatique [Termes IGN] extraction de traits caractéristiques [Termes IGN] image à haute résolution [Termes IGN] image Worldview [Termes IGN] segmentation sémantique Résumé : (Auteur) The analysis of built-up areas has always been a popular research topic for remote sensing applications. However, automatic extraction of built-up areas from a wide range of regions remains challenging. In this article, a fully convolutional network (FCN)–based strategy is proposed to address built-up area extraction. The proposed algorithm can be divided into two main steps. First, divide the remote sensing image into blocks and extract their deep features by a lightweight multi-branch convolutional neural network (LMB-CNN). Second, rearrange the deep features into feature maps that are fed into a well-designed FCN for image segmentation. Our FCN is integrated with multi-branch blocks and outputs multi-channel segmentation masks that are utilized to balance the false alarm and missing alarm. Experiments demonstrate that the overall classification accuracy of the proposed algorithm can achieve 98.75% in the test data set and that it has a faster processing compared with the existing state-of-the-art algorithms. Numéro de notice : A2019-522 Affiliation des auteurs : non IGN Thématique : IMAGERIE/URBANISME Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.14358/PERS.85.10.737 Date de publication en ligne : 01/10/2019 En ligne : https://doi.org/10.14358/PERS.85.10.737 Format de la ressource électronique : URL Article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=93992 [article]

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Hierarchical method of urban building extraction inspired by human perception / Chao Tao in Photogrammetric Engineering & Remote Sensing, PERS, vol 79 n° 12 (December 2013)  

Public [article]  inPhotogrammetric Engineering & Remote Sensing, PERS > vol 79 n° 12 (December 2013) . - pp 1109 - 1119 Titre : Hierarchical method of urban building extraction inspired by human perception Type de document : Article/Communication Auteurs : Chao Tao, Auteur ; Yihua Tan, Auteur ; Zheng-Rong Zou, Auteur Année de publication : 2013 Article en page(s) : pp 1109 - 1119 Note générale : Bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] classification bayesienne [Termes IGN] détection du bâti [Termes IGN] image à haute résolution [Termes IGN] morphologie mathématique [Termes IGN] reconnaissance de formes [Termes IGN] segmentation d'image Résumé : (Auteur) In a high-resolution satellite image, buildings can be considered as clustered objects belonging to the same category. Human perception of such objects consists of an initial identification of simple instances followed by recognition of more complicated ones by deduction. Inspired by this observation, a hierarchical building extraction framework is proposed to simulate the process, which includes three major components. First, a total variation-based segmentation algorithm is presented to decompose the given image into object-level elements. Then, shape analysis is applied to extract some common and easily identified rectangular buildings. Finally, the detection of buildings with complex structures is formulated as a deduction problem based on preceding extracted information in terms of maximum a posteriori (MAP) estimation, and a Bayesian based approach is proposed to deal with it. The experimental results demonstrate that the proposed framework is capable of efficiently identifying urban buildings from high-resolution satellite images. Numéro de notice : A2013-689 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.14358/PERS.79.12.1109 En ligne : https://doi.org/10.14358/PERS.79.12.1109 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=32825 [article]