{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,2]],"date-time":"2026-07-02T05:07:22Z","timestamp":1782968842887,"version":"3.54.5"},"reference-count":47,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2022,12,12]],"date-time":"2022-12-12T00:00:00Z","timestamp":1670803200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Sichuan Provincial Department of Science and Technology","award":["2022NSFSC0172"],"award-info":[{"award-number":["2022NSFSC0172"]}]},{"name":"Sichuan Provincial Department of Science and Technology","award":["202210626054"],"award-info":[{"award-number":["202210626054"]}]},{"name":"Sichuan Agricultural University Innovation Training Programme Project","award":["2022NSFSC0172"],"award-info":[{"award-number":["2022NSFSC0172"]}]},{"name":"Sichuan Agricultural University Innovation Training Programme Project","award":["202210626054"],"award-info":[{"award-number":["202210626054"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Agricultural land extraction is an essential technical means to promote sustainable agricultural development and modernization research. Existing supervised algorithms rely on many finely annotated remote-sensing images, which is both time-consuming and expensive. One way to reduce the annotation cost approach is to migrate models trained on existing annotated data (source domain) to unannotated data (target domain). However, model generalization capability is often unsatisfactory due to the limit of the domain gap. In this work, we use an unsupervised adversarial domain adaptation method to train a neural network to close the gap between the source and target domains for unsupervised agricultural land extraction. The overall approach consists of two phases: inter-domain and intra-domain adaptation. In the inter-domain adaptation, we use a generative adversarial network (GAN) to reduce the inter-domain gap between the source domain (labeled dataset) and the target domain (unlabeled dataset). The transformer with robust long-range dependency modeling acts as the backbone of the generator. In addition, the multi-scale feature fusion (MSFF) module is designed in the generator to accommodate remote sensing datasets with different spatial resolutions. Further, we use an entropy-based approach to divide the target domain. The target domain is divided into two subdomains, easy split images and hard split images. By training against each other between the two subdomains, we reduce the intra-domain gap. Experiments results on the \u201cDeepGlobe \u2192 LoveDA\u201d, \u201cGID \u2192 LoveDA\u201d and \u201cDeepGlobe \u2192 GID\u201d unsupervised agricultural land extraction tasks demonstrate the effectiveness of our method and its superiority to other unsupervised domain adaptation techniques.<\/jats:p>","DOI":"10.3390\/rs14246298","type":"journal-article","created":{"date-parts":[[2022,12,13]],"date-time":"2022-12-13T03:32:32Z","timestamp":1670902352000},"page":"6298","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Unsupervised Adversarial Domain Adaptation for Agricultural Land Extraction of Remote Sensing Images"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1313-9154","authenticated-orcid":false,"given":"Junbo","family":"Zhang","sequence":"first","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Ya\u2019an 625000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shifeng","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Ya\u2019an 625000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jun","family":"Sun","sequence":"additional","affiliation":[{"name":"College of Resources, Sichuan Agricultural University, Chengdu 611130, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dinghua","family":"Ou","sequence":"additional","affiliation":[{"name":"College of Resources, Sichuan Agricultural University, Chengdu 611130, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaobo","family":"Wu","sequence":"additional","affiliation":[{"name":"College of Resources, Sichuan Agricultural University, Chengdu 611130, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mantao","family":"Wang","sequence":"additional","affiliation":[{"name":"College of Information Engineering, Sichuan Agricultural University, Ya\u2019an 625000, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"111402","DOI":"10.1016\/j.rse.2019.111402","article-title":"Remote sensing for agricultural applications: A meta-review","volume":"236","author":"Weiss","year":"2020","journal-title":"Remote Sens. 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