A Triple-Stream Network With Cross-Stage Feature Fusion for High-Resolution Image Change Detection
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Le résumé fourni par la source
Change detection (CD) based on high-resolution remote sensing images can be used to monitor land cover changes, which is an important and challenging topic in the remote sensing field. In recent years, with the development of deep learning, CD methods based on deep learning have achieved good results in the field of CD. However, most current CD methods use single- or dual-stream networks to extract change features, which is insufficient to extract and learn bitemporal change information thoroughly. This article proposes a triple-stream network (TSNet) with cross-stage feature fusion for CD in high-resolution bitemporal remote sensing images. First, to obtain highly representative deep features in the original image, we perform feature extraction on bitemporal remote sensing images and their concatenated image with a dual-stream encoder and a single-stream encoder, respectively. Then, the bitemporal multiscale features extracted by the dual-stream encoder are input into a multistage bidirectional convolutional gated recurrent unit (MSBC_GRU) feature fusion module, allowing the network to learn the change information in a cross-stage manner. In addition, we use a dual-channel attention module to fuse the features extracted by dual- and single-stream encoders, improving the network’s ability to discriminate changed features. The effectiveness of TSNet is demonstrated with three publicly available CD datasets. The extensive experimental results demonstrate that the proposed method achieves the state-of-the-art CD performance on the above three datasets.
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Le contrôle bibliographique ouvert
DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.
- Titre Crossref
- A Triple-Stream Network With Cross-Stage Feature Fusion for High-Resolution Image Change Detection
- Date Crossref
- 01/01/2023
- 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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Chinese Academy of Sciences pays non établi dans la noticeOrganisme public
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Aerospace Information Research Institute Airborne Remote Sensing Center pays non établi dans la noticeUniversité ou école supérieure
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University of Chinese Academy of Sciences pays non établi dans la noticeUniversité ou école supérieure
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College of Resources and Environment pays non établi dans la noticeUniversité ou école supérieure
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Key Laboratory of Digital Earth Science pays non établi dans la noticeStructure de recherche
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China Urban Development Planning and Design Consulting Company Ltd. pays non établi dans la noticeEntreprise
Chinese Academy of Sciences, Airborne Remote Sensing Center — Aerospace Information Research Institute et University of Chinese Academy of Sciences, avec 3 autres affiliations.
Une affiliation ne permet pas de déduire la nationalité d’un auteur.