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.
|Number of pages||9|
|Journal||IEEE Transactions on Geoscience and Remote Sensing|
|Publication status||Published - 1993 Jul|
Bibliographical noteFunding Information:
Manuscript received September 8, 1992; revised March 1, 1993. This work was supported in part by NASA under Grant NAGW-925. The authors are with the School of Electrical Engineering, Purdue University, W. Lafayette, IN 47907. IEEE Log Number 9209089.
All Science Journal Classification (ASJC) codes
- Electrical and Electronic Engineering
- Earth and Planetary Sciences(all)