Analyzing High-Dimensional Multispectral Data

Chul Hee Lee, David A. Landgrebe

Research output: Contribution to journalArticle

162 Citations (Scopus)

Abstract

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
Volume31
Issue number4
DOIs
Publication statusPublished - 1993 Jan 1

Fingerprint

Statistics
Classifiers
visualization
Visualization
statistics
Color

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Earth and Planetary Sciences(all)

Cite this

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Analyzing High-Dimensional Multispectral Data. / Lee, Chul Hee; Landgrebe, David A.

In: IEEE Transactions on Geoscience and Remote Sensing, Vol. 31, No. 4, 01.01.1993, p. 792-800.

Research output: Contribution to journalArticle

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