Simple Multiscale UNet for Change Detection With Heterogeneous Remote Sensing Images
Rattachement africain : cn, is. Niveau de preuve : code pays fourni par la source.
Le résumé fourni par la source
Change detection with heterogeneous remote sensing images (HRSIs) is attractive for observing the Earth’s surface when homogeneous images are unavailable. However, HRSIs cannot be compared directly because the imaging mechanisms for bitemporal HRSIs are different, and detecting change with HRSIs is challenging. In this letter, a simple yet effective deep learning approach based on the classical UNet is proposed. First, a pair of image patches are concatenated together to learn a shared abstract feature in both image patch domains. Then, a multiscale convolution module is embedded in a UNet backbone to cover the various sizes and shapes of ground targets in an image scene. Finally, a combined loss function, which incorporates the focal and dice losses with an adjustable parameter, was incorporated to alleviate the effect of the imbalanced quantity of positive and negative samples in the training progress. By comparisons with five state-of-the-art methods in three pairs of real HRSIs, the experimental results achieved by our proposed approach have the best overall accuracy (OA), average accuracy (AA), recall (RC), and F-Score that are more than 95%, 79%, 60%, and 61%, respectively. The quantitative results and visual performance indicated the feasibility and superiority of the proposed approach for detecting land cover change with HRSIs.
Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.
Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- Simple Multiscale UNet for Change Detection With Heterogeneous Remote Sensing Images
- Date Crossref
- 01/01/2022
- Éditeur
- Institute of Electrical and Electronics Engineers (IEEE)
- Type
- journal-article
Ce recoupement confirme des métadonnées liées au DOI. Il ne confirme ni la méthode ni les conclusions de l’étude, et il ne compte pas comme une seconde source scientifique indépendante.
Où se fait cette recherche
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Northwestern Polytechnical University Research and Development Institute pays non établi dans la noticeUniversité ou école supérieure
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University of Iceland pays non établi dans la noticeUniversité ou école supérieure
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School of Computer Science and Engineering pays non établi dans la noticeUniversité ou école supérieure
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School of Software pays non établi dans la noticeUniversité ou école supérieure
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Faculty of Electrical and Computer Engineering pays non établi dans la noticeUniversité ou école supérieure
Research and Development Institute — Northwestern Polytechnical University, University of Iceland et School of Computer Science and Engineering, avec 2 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.