{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,30]],"date-time":"2026-03-30T14:14:28Z","timestamp":1774880068084,"version":"3.50.1"},"reference-count":4,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2012,7,31]],"date-time":"2012-07-31T00:00:00Z","timestamp":1343692800000},"content-version":"vor","delay-in-days":212,"URL":"http:\/\/creativecommons.org\/licenses\/by\/3.0\/"}],"funder":[{"name":"Paul Ivanier Center"},{"DOI":"10.13039\/501100002946","name":"German Aerospace Center","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100002946","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100002749","name":"Belgian Science Policy Office","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100002749","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Electrical and Computer Engineering"],"published-print":{"date-parts":[[2012,1]]},"abstract":"<jats:p>Accurate covariance matrix estimation for high\u2010dimensional data can be a difficult problem. A good approximation of the covariance matrix needs in most cases a prohibitively large number of pixels, that is, pixels from a stationary section of the image whose number is greater than several times the number of bands. Estimating the covariance matrix with a number of pixels that is on the order of the number of bands or less will cause not only a bad estimation of the covariance matrix  but also a singular covariance matrix which cannot be inverted. In this paper we will investigate two methods to give a sufficient approximation for the covariance matrix while only using a small number of neighboring pixels. The first is the quasilocal covariance matrix (QLRX) that uses the variance of the global covariance instead of the variances that are too small and cause a singular covariance. The second method is sparse matrix transform (SMT) that performs a set of K\u2010givens rotations to estimate the covariance matrix. We will compare results from target acquisition that are based on both of these methods. An improvement for the SMT algorithm is suggested.<\/jats:p>","DOI":"10.1155\/2012\/628479","type":"journal-article","created":{"date-parts":[[2012,7,31]],"date-time":"2012-07-31T21:01:23Z","timestamp":1343768483000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Target Detection Using Nonsingular Approximations for a Singular Covariance Matrix"],"prefix":"10.1155","volume":"2012","author":[{"given":"Nir","family":"Gorelik","sequence":"first","affiliation":[]},{"given":"Dan","family":"Blumberg","sequence":"additional","affiliation":[]},{"given":"Stanley R.","family":"Rotman","sequence":"additional","affiliation":[]},{"given":"Dirk","family":"Borghys","sequence":"additional","affiliation":[]}],"member":"311","published-online":{"date-parts":[[2012,7,31]]},"reference":[{"key":"e_1_2_8_1_2","doi-asserted-by":"publisher","DOI":"10.1117\/1.2759894"},{"key":"e_1_2_8_2_2","doi-asserted-by":"publisher","DOI":"10.1117\/1.2965814"},{"key":"e_1_2_8_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/JSTSP.2010.2103924"},{"key":"e_1_2_8_4_2","doi-asserted-by":"crossref","unstructured":"BorghysD.andPerneelC. Study of the influence of pre-processing on local statistics-based anomaly detector results Proceedings of the Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS \u203210) June 2010 1\u20134 2-s2.0-78649274123 https:\/\/doi.org\/10.1109\/WHISPERS.2010.5594922.","DOI":"10.1109\/WHISPERS.2010.5594922"}],"container-title":["Journal of Electrical and Computer Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2012\/628479.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2012\/628479.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1155\/2012\/628479","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,6,12]],"date-time":"2024-06-12T12:14:05Z","timestamp":1718194445000},"score":1,"resource":{"primary":{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/10.1155\/2012\/628479"}},"subtitle":[],"editor":[{"given":"Xiaofei","family":"Hu","sequence":"additional","affiliation":[]}],"short-title":[],"issued":{"date-parts":[[2012,1]]},"references-count":4,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2012,1]]}},"alternative-id":["10.1155\/2012\/628479"],"URL":"https:\/\/doi.org\/10.1155\/2012\/628479","archive":["Portico"],"relation":{},"ISSN":["2090-0147","2090-0155"],"issn-type":[{"value":"2090-0147","type":"print"},{"value":"2090-0155","type":"electronic"}],"subject":[],"published":{"date-parts":[[2012,1]]},"assertion":[{"value":"2012-04-01","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2012-06-07","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2012-07-31","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"628479"}}