{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T18:25:43Z","timestamp":1771698343199,"version":"3.50.1"},"reference-count":41,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T00:00:00Z","timestamp":1617840000000},"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 China","doi-asserted-by":"publisher","award":["U19B2037"],"award-info":[{"award-number":["U19B2037"]}],"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>Accurate change detection in optical aerial images by using deep learning techniques has been attracting lots of research efforts in recent years. Correct change-detection results usually involve both global and local deep learning features. Existing deep learning approaches have achieved good performance on this task. However, under the scenarios of containing multiscale change areas within a bi-temporal image pair, existing methods still have shortcomings in adapting these change areas, such as false detection and limited completeness in detected areas. To deal with these problems, we design a hierarchical dynamic fusion network (HDFNet) to implement the optical aerial image-change detection task. Specifically, we propose a change-detection framework with hierarchical fusion strategy to provide sufficient information encouraging for change detection and introduce dynamic convolution modules to self-adaptively learn from this information. Also, we use a multilevel supervision strategy with multiscale loss functions to supervise the training process. Comprehensive experiments are conducted on two benchmark datasets, LEBEDEV and LEVIR-CD, to verify the effectiveness of the proposed method and the experimental results show that our model achieves state-of-the-art performance.<\/jats:p>","DOI":"10.3390\/rs13081440","type":"journal-article","created":{"date-parts":[[2021,4,8]],"date-time":"2021-04-08T21:27:44Z","timestamp":1617917264000},"page":"1440","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":65,"title":["HDFNet: Hierarchical Dynamic Fusion Network for Change Detection in Optical Aerial Images"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1751-3836","authenticated-orcid":false,"given":"Yi","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer Science, National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Shaanxi Provincial Key Laboratory of Speech &amp; Image Information Processing, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]},{"given":"Lei","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Computer Science, National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Shaanxi Provincial Key Laboratory of Speech &amp; Image Information Processing, Northwestern Polytechnical University, Xi\u2019an 710129, China"},{"name":"Shaanxi Satellite Application Center for Natural Resources, Xi\u2019an 710065, China"}]},{"given":"Ying","family":"Li","sequence":"additional","affiliation":[{"name":"School of Computer Science, National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Shaanxi Provincial Key Laboratory of Speech &amp; Image Information Processing, Northwestern Polytechnical University, Xi\u2019an 710129, China"},{"name":"School of Communication and Information Engineering, Xi\u2019an University of Posts and Telecommunications, Xi\u2019an 710121, China"}]},{"given":"Yanning","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Computer Science, National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, Shaanxi Provincial Key Laboratory of Speech &amp; Image Information Processing, Northwestern Polytechnical University, Xi\u2019an 710129, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,4,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"183","DOI":"10.1016\/j.isprsjprs.2020.06.003","article-title":"A deeply supervised image fusion network for change detection in high resolution bi-temporal remote sensing images","volume":"166","author":"Zhang","year":"2020","journal-title":"ISPRS J. 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