Analyzing High-Dimensional Multispectral Data

Chulhee Lee, David A. Landgrebe

Research output: Contribution to journalArticlepeer-review

177 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)792-800
Number of pages9
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number4
Publication statusPublished - 1993 Jul

Bibliographical note

Funding 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)


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