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FuNet: A novel road extraction network with fusion of location data and remote sensing imagery / Kai Zhou in ISPRS International journal of geo-information, vol 10 n° 1 (January 2021)  

Public [article]  inISPRS International journal of geo-information > vol 10 n° 1 (January 2021) . - n° 10 Titre : FuNet: A novel road extraction network with fusion of location data and remote sensing imagery Type de document : Article/Communication Auteurs : Kai Zhou, Auteur ; Yan Xie, Auteur ; Zhan Gao, Auteur ; et al., Auteur Année de publication : 2021 Article en page(s) : n° 10 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] amélioration du contraste [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] connexité (topologie) [Termes IGN] extraction du réseau routier [Termes IGN] fusion d'images [Termes IGN] itération [Termes IGN] Pékin (Chine) [Termes IGN] segmentation sémantique Résumé : (auteur) Road semantic segmentation is unique and difficult. Road extraction from remote sensing imagery often produce fragmented road segments leading to road network disconnection due to the occlusion of trees, buildings, shadows, cloud, etc. In this paper, we propose a novel fusion network (FuNet) with fusion of remote sensing imagery and location data, which plays an important role of location data in road connectivity reasoning. A universal iteration reinforcement (IteR) module is embedded into FuNet to enhance the ability of network learning. We designed the IteR formula to repeatedly integrate original information and prediction information and designed the reinforcement loss function to control the accuracy of road prediction output. Another contribution of this paper is the use of histogram equalization data pre-processing to enhance image contrast and improve the accuracy by nearly 1%. We take the excellent D-LinkNet as the backbone network, designing experiments based on the open dataset. The experiment result shows that our method improves over the compared advanced road extraction methods, which not only increases the accuracy of road extraction, but also improves the road topological connectivity. Numéro de notice : A2021-147 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article nature-HAL : ArtAvecCL-RevueIntern DOI : 10.3390/ijgi10010039 Date de publication en ligne : 19/01/2021 En ligne : https://doi.org/10.3390/ijgi10010039 Format de la ressource électronique : url article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=97055 [article]