{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T22:59:31Z","timestamp":1773269971317,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,13]],"date-time":"2024-06-13T00:00:00Z","timestamp":1718236800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62276197"],"award-info":[{"award-number":["62276197"]}]},{"name":"National Natural Science Foundation of China","award":["62006178"],"award-info":[{"award-number":["62006178"]}]},{"name":"National Natural Science Foundation of China","award":["62171332"],"award-info":[{"award-number":["62171332"]}]},{"name":"National Natural Science Foundation of China","award":["2023-CX-TD-09"],"award-info":[{"award-number":["2023-CX-TD-09"]}]},{"name":"Key Research and Development Program in the Shaanxi Province Innovation Capability Support Plan","award":["62276197"],"award-info":[{"award-number":["62276197"]}]},{"name":"Key Research and Development Program in the Shaanxi Province Innovation Capability Support Plan","award":["62006178"],"award-info":[{"award-number":["62006178"]}]},{"name":"Key Research and Development Program in the Shaanxi Province Innovation Capability Support Plan","award":["62171332"],"award-info":[{"award-number":["62171332"]}]},{"name":"Key Research and Development Program in the Shaanxi Province Innovation Capability Support Plan","award":["2023-CX-TD-09"],"award-info":[{"award-number":["2023-CX-TD-09"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Cross-domain scene classification requires the transfer of knowledge from labeled source domains to unlabeled target domain data to improve its classification performance. This task can reduce the labeling cost of remote sensing images and improve the generalization ability of models. However, the huge distributional gap between labeled source domains and unlabeled target domains acquired by different scenes and different sensors is a core challenge. Existing cross-domain scene classification methods focus on designing better distributional alignment constraints, but are under-explored for fine-grained features. We propose a cross-domain scene classification method called the Frequency Component Adaptation Network (FCAN), which considers low-frequency features and high-frequency features separately for more comprehensive adaptation. Specifically, the features are refined and aligned separately through a high-frequency feature enhancement module (HFE) and a low-frequency feature extraction module (LFE). We conducted extensive transfer experiments on 12 cross-scene tasks between the AID, CLRS, MLRSN, and RSSCN7 datasets, as well as two cross-sensor tasks between the NWPU-RESISC45 and NaSC-TG2 datasets, and the results show that the FCAN can effectively improve the model\u2019s performance for scene classification on unlabeled target domains compared to other methods.<\/jats:p>","DOI":"10.3390\/rs16122134","type":"journal-article","created":{"date-parts":[[2024,6,13]],"date-time":"2024-06-13T04:30:12Z","timestamp":1718253012000},"page":"2134","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Cross-Domain Classification Based on Frequency Component Adaptation for Remote Sensing Images"],"prefix":"10.3390","volume":"16","author":[{"given":"Peng","family":"Zhu","sequence":"first","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0379-2042","authenticated-orcid":false,"given":"Xiangrong","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Xiao","family":"Han","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7319-1635","authenticated-orcid":false,"given":"Xina","family":"Cheng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Jing","family":"Gu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5472-1426","authenticated-orcid":false,"given":"Puhua","family":"Chen","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]},{"given":"Licheng","family":"Jiao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education, School of Artificial Intelligence, Xidian University, Xi\u2019an 710071, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1109\/LGRS.2017.2731997","article-title":"Remote sensing image scene classification using bag of convolutional features","volume":"14","author":"Cheng","year":"2017","journal-title":"IEEE Geosci. 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