{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,29]],"date-time":"2026-01-29T13:31:52Z","timestamp":1769693512696,"version":"3.49.0"},"reference-count":21,"publisher":"Wiley","license":[{"start":{"date-parts":[[2020,8,25]],"date-time":"2020-08-25T00:00:00Z","timestamp":1598313600000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Ningxia Hui Autonomous Region Key Research and Development Program of Ningxia Hui Autonomous Region","award":["2019BFG02009"],"award-info":[{"award-number":["2019BFG02009"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computational Intelligence and Neuroscience"],"published-print":{"date-parts":[[2020,8,25]]},"abstract":"<jats:p>In recent years, image processing methods based on convolutional neural networks (CNNs) have achieved very good results. At the same time, many branch techniques have been proposed to improve accuracy. Aiming at the change detection task of remote sensing images, we propose a new network based on U-Net in this paper. The attention mechanism is cleverly applied in the change detection task, and the data-dependent upsampling (DUpsampling) method is used at the same time, so that the network shows improvement in accuracy, and the calculation amount is greatly reduced. The experimental results show that, in the two-phase images of Yinchuan City, the proposed network has a better antinoise ability and can avoid false detection to a certain extent.<\/jats:p>","DOI":"10.1155\/2020\/6430627","type":"journal-article","created":{"date-parts":[[2020,8,25]],"date-time":"2020-08-25T23:32:24Z","timestamp":1598398344000},"page":"1-11","source":"Crossref","is-referenced-by-count":21,"title":["Change Detection of Remote Sensing Images Based on Attention Mechanism"],"prefix":"10.1155","volume":"2020","author":[{"given":"Long","family":"Chen","sequence":"first","affiliation":[{"name":"School of Computer and Communication Engineering, University of Science and Technology Beijing (USTB), No. 30 Xueyuan Road, Haidian District, Beijing 100083, China"},{"name":"Beijing Key Laboratory of Knowledge Engineering for Materials Science, Beijing, 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