Analyzing high-dimensional multispectral data

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.

Abstract

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.>