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Analyzing High-Dimensional Multispectral DataIn this paper, through a series of specific examples, we illustrate some characteristics encountered in analyzing high- dimensional multispectral data. The increased importance of the second-order statistics in analyzing high-dimensional data is illustrated, as is the shortcoming of classifiers such as the minimum distance classifier which rely on first-order variations alone. We also illustrate how inaccurate estimation or first- and second-order statistics, e.g., from use of training sets which are too small, affects the performance of a classifier. Recognizing the importance of second-order statistics on the one hand, but the increased difficulty in perceiving and comprehending information present in statistics derived from high-dimensional data on the other, we propose a method to aid visualization of high-dimensional statistics using a color coding scheme.
Document ID
19970022499
Acquisition Source
Headquarters
Document Type
Reprint (Version printed in journal)
External Source(s)
Authors
Lee, Chulhee
(Purdue Univ. West Lafayette, IN United States)
Landgrebe, David A.
(Purdue Univ. West Lafayette, IN United States)
Date Acquired
August 17, 2013
Publication Date
July 1, 1993
Publication Information
Publication: IEEE Transactions on Geoscience and Remote Sensing
Publisher: IEEE
Volume: 31
Issue: 4
ISSN: 0196-2892
Subject Category
Earth Resources And Remote Sensing
Report/Patent Number
IEEE-9209089
NAS 1.26:204332
NASA-CR-204332
Accession Number
97N72122
Funding Number(s)
CONTRACT_GRANT: NAGw-925
Distribution Limits
Public
Copyright
Public Use Permitted.
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