{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,19]],"date-time":"2026-05-19T23:51:31Z","timestamp":1779234691500,"version":"3.51.4"},"reference-count":57,"publisher":"MDPI AG","issue":"24","license":[{"start":{"date-parts":[[2019,12,16]],"date-time":"2019-12-16T00:00:00Z","timestamp":1576454400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["XJS17106 and JB180502"],"award-info":[{"award-number":["XJS17106 and JB180502"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["51801142"],"award-info":[{"award-number":["51801142"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100007128","name":"Natural Science Foundation of Shaanxi Province","doi-asserted-by":"publisher","award":["2018JQ5022"],"award-info":[{"award-number":["2018JQ5022"]}],"id":[{"id":"10.13039\/501100007128","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Hyperspectral anomaly detection methods are often limited by the effects of redundant information and isolated noise. Here, a novel hyperspectral anomaly detection method based on harmonic analysis (HA) and low rank decomposition is proposed. This paper introduces three main innovations: first and foremost, in order to extract low-order harmonic images, a single-pixel-related HA was introduced to reduce dimension and remove redundant information in the original hyperspectral image (HSI). Additionally, adopting the guided filtering (GF) and differential operation, a novel background dictionary construction method was proposed to acquire the initial smoothed images suppressing some isolated noise, while simultaneously constructing a discriminative background dictionary. Last but not least, the original HSI was replaced by the initial smoothed images for a low-rank decomposition via the background dictionary. This operation took advantage of the low-rank attribute of background and the sparse attribute of anomaly. We could finally get the anomaly objectives through the sparse matrix calculated from the low-rank decomposition. The experiments compared the detection performance of the proposed method and seven state-of-the-art methods in a synthetic HSI and two real-world HSIs. Besides qualitative assessment, we also plotted the receiver operating characteristic (ROC) curve of each method and report the respective area under the curve (AUC) for quantitative comparison. Compared with the alternative methods, the experimental results illustrated the superior performance and satisfactory results of the proposed method in terms of visual characteristics, ROC curves and AUC values.<\/jats:p>","DOI":"10.3390\/rs11243028","type":"journal-article","created":{"date-parts":[[2019,12,17]],"date-time":"2019-12-17T02:59:01Z","timestamp":1576551541000},"page":"3028","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Hyperspectral Anomaly Detection with Harmonic Analysis and Low-Rank Decomposition"],"prefix":"10.3390","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1895-1894","authenticated-orcid":false,"given":"Pei","family":"Xiang","sequence":"first","affiliation":[{"name":"School of Physics and Optoelectronic Engineering, Xidian University, No. 2, South Taibai Road, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1497-4810","authenticated-orcid":false,"given":"Jiangluqi","family":"Song","sequence":"additional","affiliation":[{"name":"School of Physics and Optoelectronic Engineering, Xidian University, No. 2, South Taibai Road, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Physics and Optoelectronic Engineering, Xidian University, No. 2, South Taibai Road, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Gu","sequence":"additional","affiliation":[{"name":"National Institute of Informatics, Tokyo 101-8340, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huixin","family":"Zhou","sequence":"additional","affiliation":[{"name":"School of Physics and Optoelectronic Engineering, Xidian University, No. 2, South Taibai Road, Xi\u2019an 710071, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2019,12,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1109\/MGRS.2013.2244672","article-title":"Hyperspectral remote sensing data analysis and future challenges","volume":"1","author":"Plaza","year":"2013","journal-title":"IEEE Geosci. 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