{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T05:16:15Z","timestamp":1780636575323,"version":"3.54.1"},"reference-count":64,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"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":["61825205"],"award-info":[{"award-number":["61825205"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61772459"],"award-info":[{"award-number":["61772459"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Research and Development Program of Zhejiang Provence, China","award":["2021C01017"],"award-info":[{"award-number":["2021C01017"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>When segmenting massive amounts of remote sensing images collected from different satellites or geographic locations (cities), the pre-trained deep learning models cannot always output satisfactory predictions. To deal with this issue, domain adaptation has been widely utilized to enhance the generalization abilities of the segmentation models. Most of the existing domain adaptation methods, which based on image-to-image translation, firstly transfer the source images to the pseudo-target images, adapt the classifier from the source domain to the target domain. However, these unidirectional methods suffer from the following two limitations: (1) they do not consider the inverse procedure and they cannot fully take advantage of the information from the other domain, which is also beneficial, as confirmed by our experiments; (2) these methods may fail in the cases where transferring the source images to the pseudo-target images is difficult. In this paper, in order to solve these problems, we propose a novel framework BiFDANet for unsupervised bidirectional domain adaptation in the semantic segmentation of remote sensing images. It optimizes the segmentation models in two opposite directions. In the source-to-target direction, BiFDANet learns to transfer the source images to the pseudo-target images and adapts the classifier to the target domain. In the opposite direction, BiFDANet transfers the target images to the pseudo-source images and optimizes the source classifier. At test stage, we make the best of the source classifier and the target classifier, which complement each other with a simple linear combination method, further improving the performance of our BiFDANet. Furthermore, we propose a new bidirectional semantic consistency loss for our BiFDANet to maintain the semantic consistency during the bidirectional image-to-image translation process. The experiments on two datasets including satellite images and aerial images demonstrate the superiority of our method against existing unidirectional methods.<\/jats:p>","DOI":"10.3390\/rs14010190","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:06:15Z","timestamp":1641769575000},"page":"190","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":38,"title":["BiFDANet: Unsupervised Bidirectional Domain Adaptation for Semantic Segmentation of Remote Sensing Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2784-6607","authenticated-orcid":false,"given":"Yuxiang","family":"Cai","sequence":"first","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yingchun","family":"Yang","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qiyi","family":"Zheng","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7816-5081","authenticated-orcid":false,"given":"Zhengwei","family":"Shen","sequence":"additional","affiliation":[{"name":"Research Institute of Advanced Technology, Zhejiang University, Hangzhou 310027, China"},{"name":"Deqing Institute of Advanced Technology and Industrialization, Zhejiang University, Huzhou 313200, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8550-6710","authenticated-orcid":false,"given":"Yongheng","family":"Shang","sequence":"additional","affiliation":[{"name":"Research Institute of Advanced Technology, Zhejiang University, Hangzhou 310027, China"},{"name":"Deqing Institute of Advanced Technology and Industrialization, Zhejiang University, Huzhou 313200, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianwei","family":"Yin","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Zhejiang University, Hangzhou 310027, China"},{"name":"School of Software Technology, Zhejiang University, Ningbo 315048, China"},{"name":"China Institute for New Urbanization Studies, Huzhou 313000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhongtian","family":"Shi","sequence":"additional","affiliation":[{"name":"Hangzhou Planning and Natural Resources Survey and Monitoring Center, Hangzhou 310012, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Chen, Z., Li, D., Fan, W., Guan, H., Wang, C., and Li, J. 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