001     53329
005     20180211171255.0
024 7 _ |2 DOI
|a 10.1080/01431160500222608
024 7 _ |2 WOS
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037 _ _ |a PreJuSER-53329
041 _ _ |a eng
082 _ _ |a 620
084 _ _ |2 WoS
|a Remote Sensing
084 _ _ |2 WoS
|a Imaging Science & Photographic Technology
100 1 _ |a Canty, M. J.
|b 0
|u FZJ
|0 P:(DE-Juel1)VDB4989
245 _ _ |a Visualization and unsupervised classification of changes in multispectral satellite imagery
260 _ _ |a London
|b Taylor & Francis
|c 2006
300 _ _ |a 3961 - 3975
336 7 _ |a Journal Article
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336 7 _ |a Journal Article
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336 7 _ |a ARTICLE
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336 7 _ |a JOURNAL_ARTICLE
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336 7 _ |a article
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440 _ 0 |a International Journal of Remote Sensing
|x 0143-1161
|0 2845
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|v 27
500 _ _ |a Record converted from VDB: 12.11.2012
520 _ _ |a The statistical techniques of multivariate alteration detection, minimum/maximum autocorrelation factors transformation, expectation maximization and probabilistic label relaxation are combined in a unified scheme to visualize and to classify changes in multispectral satellite data. The methods are demonstrated with an example involving bitemporal LANDSAT TM imagery.
536 _ _ |a Terrestrische Umwelt
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588 _ _ |a Dataset connected to Web of Science
650 _ 7 |a J
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700 1 _ |a Nielsen, A. A.
|b 1
|0 P:(DE-HGF)0
773 _ _ |a 10.1080/01431160500222608
|g Vol. 27, p. 3961 - 3975
|p 3961 - 3975
|q 27<3961 - 3975
|0 PERI:(DE-600)1497529-4
|t International journal of remote sensing
|v 27
|y 2006
|x 0143-1161
856 7 _ |u http://dx.doi.org/10.1080/01431160500222608
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914 1 _ |y 2006
915 _ _ |0 StatID:(DE-HGF)0010
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981 _ _ |a I:(DE-Juel1)IBG-3-20101118


LibraryCollectionCLSMajorCLSMinorLanguageAuthor
Marc 21