Analyzing high-dimensional multispectral data

In 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 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, we propose a method to aid visualization of high dimensional statistics using a color coding scheme.