{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:21:42Z","timestamp":1760149302040,"version":"build-2065373602"},"reference-count":40,"publisher":"MDPI AG","issue":"15","license":[{"start":{"date-parts":[[2023,7,27]],"date-time":"2023-07-27T00:00:00Z","timestamp":1690416000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Projects","award":["2022YFF0904400","41830108","42201392","2022M723222"],"award-info":[{"award-number":["2022YFF0904400","41830108","42201392","2022M723222"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2022YFF0904400","41830108","42201392","2022M723222"],"award-info":[{"award-number":["2022YFF0904400","41830108","42201392","2022M723222"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["2022YFF0904400","41830108","42201392","2022M723222"],"award-info":[{"award-number":["2022YFF0904400","41830108","42201392","2022M723222"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral images can assist change-detection methods in precisely identifying differences in land cover in the same region at different observation times. However, the difficulty of labeling hyperspectral images restricts the number of training samples for supervised change-detection methods, and there are also complex real influences on hyperspectral images, such as noise and observation directions. Furthermore, current deep-learning-based change-detection methods ignore the feature reusage from receptive fields with different scales and cannot effectively suppress unrelated spatial\u2013spectral dependencies globally. To better handle these issues, a contrastive self-supervised two-domain residual attention network (TRAMNet) with a random augmentation pool is proposed for hyperspectral change detection. The contributions of this article are summarized as follows. (1) To improve the feature extraction from hyperspectral images with random Gaussian noise and directional information, a contrastive learning framework with a random data augmentation pool and a soft contrastive loss function (SCLF) is proposed. (2) The multi-scale feature fusion module (MFF) is provided to achieve feature reusage from different receptive fields. (3) A two-domain residual attention (TRA) block is designed to suppress irrelated change information and extract long-range dependencies from both spectral and spatial domains globally. Extensive experiments were carried out on three real datasets. The results show that the proposed TRAMNet can better initialize the model weights for hyperspectral change-detection task and effectively decrease the need for training samples. The proposed method outperforms most existing hyperspectral change-detection methods.<\/jats:p>","DOI":"10.3390\/rs15153739","type":"journal-article","created":{"date-parts":[[2023,7,28]],"date-time":"2023-07-28T02:08:00Z","timestamp":1690510080000},"page":"3739","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Contrastive Self-Supervised Two-Domain Residual Attention Network with Random Augmentation Pool for Hyperspectral Change Detection"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3463-1663","authenticated-orcid":false,"given":"Yixiang","family":"Huang","sequence":"first","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No. 20 Datun Road, Beijing 100101, China"},{"name":"University of Chinese Academy of Sciences, No. 3 Datun Road, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3533-9966","authenticated-orcid":false,"given":"Lifu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No. 20 Datun Road, Beijing 100101, China"}]},{"given":"Wenchao","family":"Qi","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No. 20 Datun Road, Beijing 100101, China"}]},{"given":"Changping","family":"Huang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No. 20 Datun Road, Beijing 100101, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7872-5587","authenticated-orcid":false,"given":"Ruoxi","family":"Song","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No. 20 Datun Road, Beijing 100101, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1109\/MGRS.2017.2762087","article-title":"Advances in Hyperspectral Image and Signal Processing: A Comprehensive Overview of the State of the Art","volume":"5","author":"Ghamisi","year":"2017","journal-title":"IEEE Geosci. 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