{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T21:27:05Z","timestamp":1769635625255,"version":"3.49.0"},"reference-count":41,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2018,6,30]],"date-time":"2018-06-30T00:00:00Z","timestamp":1530316800000},"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":["61572133"],"award-info":[{"award-number":["61572133"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Research fund for State Key Laboratory of Earth Surface Processes and Resource Ecology","award":["2017-KF-19"],"award-info":[{"award-number":["2017-KF-19"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Traditional target detection (TD) algorithms for hyperspectral imagery (HSI) typically suffer from background interference. To alleviate this problem, we propose a novel preprocessing method based on tensor principal component analysis (TPCA) to separate the background and target apart. With the use of TPCA, HSI is decomposed into a principal component part and a residual part with the spatial-spectral information of the HSI being fully exploited, and TD is performed on the latter. Moreover, an effective distinction in scheme can be made between a HSI tensor\u2019s spatial and spectral domains, which is in line with the physical meanings. Experimental results from both synthetic and real hyperspectral data show that the proposed method outperforms other preprocessing methods in improving the TD accuracies. Further, target detectors that combine the TPCA preprocessing approach with traditional target detection methods can achieve better results than those of state-of-the-art methods aiming at background suppression.<\/jats:p>","DOI":"10.3390\/rs10071033","type":"journal-article","created":{"date-parts":[[2018,7,2]],"date-time":"2018-07-02T10:56:52Z","timestamp":1530529012000},"page":"1033","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":53,"title":["A Preprocessing Method for Hyperspectral Target Detection Based on Tensor Principal Component Analysis"],"prefix":"10.3390","volume":"10","author":[{"given":"Zehao","family":"Chen","sequence":"first","affiliation":[{"name":"Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, China"},{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China"},{"name":"Research Center of Smart Networks and Systems, School of Information Science and Technology, Fudan University, Shanghai 200433, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9762-0788","authenticated-orcid":false,"given":"Bin","family":"Yang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, China"},{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China"},{"name":"Research Center of Smart Networks and Systems, School of Information Science and Technology, Fudan University, Shanghai 200433, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4748-6426","authenticated-orcid":false,"given":"Bin","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, China"},{"name":"State Key Laboratory of Earth Surface Processes and Resource Ecology, Beijing Normal University, Beijing 100875, China"},{"name":"Research Center of Smart Networks and Systems, School of Information Science and Technology, Fudan University, Shanghai 200433, China"}]}],"member":"1968","published-online":{"date-parts":[[2018,6,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2317","DOI":"10.1109\/JSTARS.2014.2315772","article-title":"An overview of background modeling for detection of targets and anomalies in hyperspectral remotely sensed imagery","volume":"7","author":"Matteoli","year":"2014","journal-title":"IEEE J. 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