{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T04:02:41Z","timestamp":1760241761681,"version":"build-2065373602"},"reference-count":75,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2018,9,17]],"date-time":"2018-09-17T00:00:00Z","timestamp":1537142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61231016,61303123,61273265, 61701123"],"award-info":[{"award-number":["61231016,61303123,61273265, 61701123"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["3102015JSJ0008"],"award-info":[{"award-number":["3102015JSJ0008"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Background modeling has been proven to be a promising method of hyperspectral anomaly detection. However, due to the cluttered imaging scene, modeling the background of an hyperspectral image (HSI) is often challenging. To mitigate this problem, we propose a novel structured background modeling-based hyperspectral anomaly detection method, which clearly improves the detection accuracy through exploiting the block-diagonal structure of the background. Specifically, to conveniently model the multi-mode characteristics of background, we divide the full-band patches in an HSI into different background clusters according to their spatial-spectral features. A spatial-spectral background dictionary is then learned for each cluster with a principal component analysis (PCA) learning scheme. When being represented onto those dictionaries, the background often exhibits a block-diagonal structure, while the anomalous target shows a sparse structure. In light of such an observation, we develop a low-rank representation based anomaly detection framework that can appropriately separate the sparse anomaly from the block-diagonal background. To optimize this framework effectively, we adopt the standard alternating direction method of multipliers (ADMM) algorithm. With extensive experiments on both synthetic and real-world datasets, the proposed method achieves an obvious improvement in detection accuracy, compared with several state-of-the-art hyperspectral anomaly detection methods.<\/jats:p>","DOI":"10.3390\/s18093137","type":"journal-article","created":{"date-parts":[[2018,9,17]],"date-time":"2018-09-17T10:42:20Z","timestamp":1537180940000},"page":"3137","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Structured Background Modeling for Hyperspectral Anomaly Detection"],"prefix":"10.3390","volume":"18","author":[{"given":"Fei","family":"Li","sequence":"first","affiliation":[{"name":"Shaanxi Key Lab of Speech &amp; Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi\u2019an 710129, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shaanxi Key Lab of Speech &amp; Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi\u2019an 710129, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7230-1476","authenticated-orcid":false,"given":"Xiuwei","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shaanxi Key Lab of Speech &amp; Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi\u2019an 710129, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanjia","family":"Chen","sequence":"additional","affiliation":[{"name":"Shaanxi Key Lab of Speech &amp; Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi\u2019an 710129, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dongmei","family":"Jiang","sequence":"additional","affiliation":[{"name":"Shaanxi Key Lab of Speech &amp; Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi\u2019an 710129, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Genping","family":"Zhao","sequence":"additional","affiliation":[{"name":"School of Computers, Guangdong University and Technology, Guangzhou 510006, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yanning","family":"Zhang","sequence":"additional","affiliation":[{"name":"Shaanxi Key Lab of Speech &amp; Image Information Processing (SAIIP), School of Computer Science and Engineering, Northwestern Polytechnical University, Xi\u2019an 710129, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2018,9,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Zhang, L., Wei, W., Zhang, Y., and Wang, C. 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