{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,18]],"date-time":"2026-06-18T15:43:31Z","timestamp":1781797411281,"version":"3.54.5"},"reference-count":61,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,12,4]],"date-time":"2021-12-04T00:00:00Z","timestamp":1638576000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of Shaanxi Province","award":["2021JQ-210"],"award-info":[{"award-number":["2021JQ-210"]}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["XJS200216"],"award-info":[{"award-number":["XJS200216"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Fundamental Research Funds for the Central Universities and the Innovation Fund of Xidian University","award":["Innovation Fund of Xidian University"],"award-info":[{"award-number":["Innovation Fund of Xidian University"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Joint analysis of spatial and spectral features has always been an important method for change detection in hyperspectral images. However, many existing methods cannot extract effective spatial features from the data itself. Moreover, when combining spatial and spectral features, a rough uniform global combination ratio is usually required. To address these problems, in this paper, we propose a novel attention-based spatial and spectral network with PCA-guided self-supervised feature extraction mechanism to detect changes in hyperspectral images. The whole framework is divided into two steps. First, a self-supervised mapping from each patch of the difference map to the principal components of the central pixel of each patch is established. By using the multi-layer convolutional neural network, the main spatial features of differences can be extracted. In the second step, the attention mechanism is introduced. Specifically, the weighting factor between the spatial and spectral features of each pixel is adaptively calculated from the concatenated spatial and spectral features. Then, the calculated factor is applied proportionally to the corresponding features. Finally, by the joint analysis of the weighted spatial and spectral features, the change status of pixels in different positions can be obtained. Experimental results on several real hyperspectral change detection data sets show the effectiveness and advancement of the proposed method.<\/jats:p>","DOI":"10.3390\/rs13234927","type":"journal-article","created":{"date-parts":[[2021,12,6]],"date-time":"2021-12-06T03:10:38Z","timestamp":1638760238000},"page":"4927","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Attention-Based Spatial and Spectral Network with PCA-Guided Self-Supervised Feature Extraction for Change Detection in Hyperspectral Images"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3976-7439","authenticated-orcid":false,"given":"Zhao","family":"Wang","sequence":"first","affiliation":[{"name":"Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, School of Electronic Engineering, Xidian University, No. 2 South TaiBai Road, Xi\u2019an 710075, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3714-0600","authenticated-orcid":false,"given":"Fenlong","family":"Jiang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, School of Electronic Engineering, Xidian University, No. 2 South TaiBai Road, Xi\u2019an 710075, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1394-4724","authenticated-orcid":false,"given":"Tongfei","family":"Liu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, School of Electronic Engineering, Xidian University, No. 2 South TaiBai Road, Xi\u2019an 710075, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1867-7194","authenticated-orcid":false,"given":"Fei","family":"Xie","sequence":"additional","affiliation":[{"name":"Academy of Advanced Interdisciplinary Research, Xidian University, No. 2 South TaiBai Road, Xi\u2019an 710068, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Peng","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, School of Electronic Engineering, Xidian University, No. 2 South TaiBai Road, Xi\u2019an 710075, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,12,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2365","DOI":"10.1080\/0143116031000139863","article-title":"Change detection techniques","volume":"25","author":"Lu","year":"2004","journal-title":"Int. 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