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A context feature enhancement network for building extraction from high-resolution remote sensing imagery / Jinzhi Chen in Remote sensing, vol 14 n° 9 (May-1 2022)  

Public [article]  inRemote sensing > vol 14 n° 9 (May-1 2022) . - n° 2276 Titre : A context feature enhancement network for building extraction from high-resolution remote sensing imagery Type de document : Article/Communication Auteurs : Jinzhi Chen, Auteur ; Dejun Zhang, Auteur ; Yiqi Wu, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : n° 2276 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications de télédétection [Termes IGN] apprentissage profond [Termes IGN] classification par réseau neuronal convolutif [Termes IGN] détection de contours [Termes IGN] détection du bâti [Termes IGN] image à haute résolution [Termes IGN] structure-from-motion Résumé : (auteur) The complexity and diversity of buildings make it challenging to extract low-level and high-level features with strong feature representation by using deep neural networks in building extraction tasks. Meanwhile, deep neural network-based methods have many network parameters, which take up a lot of memory and time in training and testing. We propose a novel fully convolutional neural network called the Context Feature Enhancement Network (CFENet) to address these issues. CFENet comprises three modules: the spatial fusion module, the focus enhancement module, and the feature decoder module. First, the spatial fusion module aggregates the spatial information of low-level features to obtain buildings’ outline and edge information. Secondly, the focus enhancement module fully aggregates the semantic information of high-level features to filter the information of building-related attribute categories. Finally, the feature decoder module decodes the output of the above two modules to segment the buildings more accurately. In a series of experiments on the WHU Building Dataset and the Massachusetts Building Dataset, our CFENet balances efficiency and accuracy compared to the other four methods we compared, and achieves optimality on all five evaluation metrics: PA, PC, F1, IoU, and FWIoU. This indicates that CFENet can effectively enhance and fuse buildings’ low-level and high-level features, improving building extraction accuracy. Numéro de notice : A2022-385 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.3390/rs14092276 Date de publication en ligne : 09/05/2022 En ligne : https://doi.org/10.3390/rs14092276 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=100663 [article]