{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T10:38:00Z","timestamp":1776163080480,"version":"3.50.1"},"reference-count":58,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,8,6]],"date-time":"2023-08-06T00:00:00Z","timestamp":1691280000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of PR China","doi-asserted-by":"publisher","award":["42075130"],"award-info":[{"award-number":["42075130"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Change detection is an important component in the field of remote sensing. At present, deep-learning-based change-detection methods have acquired many breakthrough results. However, current algorithms still present issues such as target misdetection, false alarms, and blurry edges. To alleviate these problems, this work proposes a network based on feature differences and attention mechanisms. This network includes a Siamese architecture-encoding network that encodes images at different times, a Difference Feature-Extraction Module (DFEM) for extracting difference features from bitemporal images, an Attention-Regulation Module (ARM) for optimizing the extracted difference features through attention, and a Cross-Scale Feature-Fusion Module (CSFM) for merging features from different encoding stages. Experimental results demonstrate that this method effectively alleviates issues of target misdetection, false alarms, and blurry edges.<\/jats:p>","DOI":"10.3390\/rs15153896","type":"journal-article","created":{"date-parts":[[2023,8,6]],"date-time":"2023-08-06T10:01:53Z","timestamp":1691316113000},"page":"3896","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["DAFNet: A Novel Change-Detection Model for High-Resolution Remote-Sensing Imagery Based on Feature Difference and Attention Mechanism"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-8192-9676","authenticated-orcid":false,"given":"Chong","family":"Ma","sequence":"first","affiliation":[{"name":"Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, B-DAT, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9254-9521","authenticated-orcid":false,"given":"Hongyang","family":"Yin","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, B-DAT, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Liguo","family":"Weng","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, B-DAT, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4681-9129","authenticated-orcid":false,"given":"Min","family":"Xia","sequence":"additional","affiliation":[{"name":"Collaborative Innovation Center on Atmospheric Environment and Equipment Technology, B-DAT, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3835-6075","authenticated-orcid":false,"given":"Haifeng","family":"Lin","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Nanjing Forestry University, Nanjing 210000, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hu, K., Zhang, E., Xia, M., Weng, L., and Lin, H. 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