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Global-aware siamese network for change detection on remote sensing images / Ruiqian Zhang in ISPRS Journal of photogrammetry and remote sensing, vol 199 (May 2023)  

Public [article]  inISPRS Journal of photogrammetry and remote sensing > vol 199 (May 2023) . - pp 61 - 72 Titre : Global-aware siamese network for change detection on remote sensing images Type de document : Article/Communication Auteurs : Ruiqian Zhang, Auteur ; Hanchao Zhang, Auteur ; Xiaogang Ning, Auteur ; et al., Auteur Année de publication : 2023 Article en page(s) : pp 61 - 72 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Traitement d'image optique [Termes IGN] analyse de sensibilité [Termes IGN] attention (apprentissage automatique) [Termes IGN] détection de changement [Termes IGN] extraction de traits caractéristiques [Termes IGN] image à haute résolution [Termes IGN] optimisation (mathématiques) [Termes IGN] réseau neuronal siamois Résumé : (auteur) Change detection (CD) in remote sensing images is one of the most important technical options to identify changes in observations in an efficient manner. CD has a wide range of applications, such as land use investigation, urban planning, environmental monitoring and disaster mapping. However, the frequently occurring class imbalance problem brings huge challenges to the change detection applications. To address this issue, we develop a novel global-aware siamese network (GAS-Net), aiming to generate global-aware features for efficient change detection by incorporating the relationships between scenes and foregrounds. The proposed GAS-Net, consisting of the global-attention module (GAM) and foreground-awareness module (FAM) that both learns contextual relationships and enhances symbiotic relation learning between scene and foreground. The experimental results demonstrate the effectiveness and robustness of the proposed GAS-Net, achieving up to 91.21% and 95.84% F1 score on two widely used public datasets, i.e., Levir-CD and Lebedev-CD dataset. The source code is available at https://github.com/xiaoxiangAQ/GAS-Net. Numéro de notice : 2023-204 Affiliation des auteurs : non IGN Thématique : IMAGERIE Nature : Article DOI : 10.1016/j.isprsjprs.2023.04.001 Date de publication en ligne : 05/04/2023 En ligne : https://doi.org/10.1016/j.isprsjprs.2023.04.001 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=103106 [article]