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Analyzing high-dimensional multispectral data

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2 Author(s)
C. Lee ; Sch. of Electr. Eng., Purdue Univ., West Lafayette, IN, USA ; D. A. Landgrebe

Through a series of specific examples, some characteristics encountered in analyzing high-dimensional multispectral data are illustrated. The increased importance of the second-order statistics in analyzing high-dimensional data is shown, as is the shortcoming of classifiers such as the minimum distance classifier, which rely on first-order variations alone. It is also shown how inaccurate estimation of 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, the authors propose a method to aid visualization of high-dimensional statistics using a color coding scheme

Published in:

IEEE Transactions on Geoscience and Remote Sensing  (Volume:31 ,  Issue: 4 )