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Scene context-driven vehicle detection in high-resolution aerial images / Chao Tao in IEEE Transactions on geoscience and remote sensing, Vol 57 n° 10 (October 2019)  

Public [article]  inIEEE Transactions on geoscience and remote sensing > Vol 57 n° 10 (October 2019) . - pp 7339 - 7351 Titre : Scene context-driven vehicle detection in high-resolution aerial images Type de document : Article/Communication Auteurs : Chao Tao, Auteur ; Li Mi, Auteur ; Yansheng Li, Auteur ; et al., Auteur Année de publication : 2019 Article en page(s) : pp 7339 - 7351 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] apprentissage profond [Termes IGN] classification orientée objet [Termes IGN] détection d'objet [Termes IGN] image à haute résolution [Termes IGN] image aérienne [Termes IGN] objet mobile [Termes IGN] véhicule automobile Résumé : (auteur) As the spatial resolution of remote sensing images is improving gradually, it is feasible to realize “scene-object” collaborative image interpretation. Unfortunately, this idea is not fully utilized in vehicle detection from high-resolution aerial images, and most of the existing methods may be promoted by considering the variability of vehicle spatial distribution in different image scenes and treating vehicle detection tasks scene-specific. With this motivation, a scene context-driven vehicle detection method is proposed in this paper. At first, we perform scene classification using the deep learning method and, then, detect vehicles in roads and parking lots separately through different vehicle detectors. Afterward, we further optimize the detection results using different postprocessing rules according to different scene types. Experimental results show that the proposed approach outperforms the state-of-the-art algorithms in terms of higher detection accuracy rate and lower false alarm rate. Numéro de notice : A2019-535 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.1109/TGRS.2019.2912985 Date de publication en ligne : 03/06/2019 En ligne : http://doi.org/10.1109/TGRS.2019.2912985 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=94131 [article]

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]