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To tackle this issue, this paper proposes a saliency-guided sparse low-rank tensor approximation model, called SSLR, to detect anomalous targets from hyperspectral remote sensing images in an unsupervised manner. Specifically, we first explore the saliency information of each pixel for regularizing the sparse anomaly matrix. We then suggest a three-directional tensor nuclear norm to obtain a low-rank background to characterize the background component. We solve the SSLR optimization problem by an efficient alternating direction method of multipliers framework. Experiments conducted on benchmark hyperspectral datasets demonstrate that the proposed SSLR outperforms some state-of-the-art anomaly detection methods. <\/jats:p>","DOI":"10.1142\/s0218126624501457","type":"journal-article","created":{"date-parts":[[2023,11,13]],"date-time":"2023-11-13T11:04:13Z","timestamp":1699873453000},"source":"Crossref","is-referenced-by-count":0,"title":["Saliency-Guided Sparse Low-Rank Tensor Approximation for Unsupervised Anomaly Detection of Hyperspectral Remote Sensing Images"],"prefix":"10.1142","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0009-0003-5126-2908","authenticated-orcid":false,"given":"ZhiGuo","family":"Du","sequence":"first","affiliation":[{"name":"School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China"},{"name":"School of Information Network Security, People\u2019s Public Security University of China, Beijing, 100038, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-0088-8999","authenticated-orcid":false,"given":"Lian","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-7361-7303","authenticated-orcid":false,"given":"MingXuan","family":"Tang","sequence":"additional","affiliation":[{"name":"School of Computer Science (National Pilot Software Engineering School), Beijing University of Posts and Telecommunications, Beijing 100876, P. R. 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