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Proceedings Paper

Progress toward information-extraction methods for hyperspectral data
Author(s): David A. Landgrebe
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Paper Abstract

A focused research program has been under way for several years to discover optimally effective means for analysis of multispectral and hyperspectral data. The methods pursued are based upon fundamental principles of signal theory and signal processing. The basic approach revolves around viewing N spectral bands of data from a pixel as a single point in N dimensional space, thus, an important aspect of the work has been to discover unique aspects of higher dimensional spaces which can be exploited for their information-bearing aspects. Substantial progress on this problem has been made in the last several years, with several key algorithms having been defined. Among these are algorithms for transforms which define optimal case-specific features, and which improve the ability of the classifier to generalize. A more fundamental finding has been to understand the characteristics of high dimensional space and the significance of design samples and their use in defining the classifier. These results have been published in separate papers over the last several years. The purpose of this paper is to survey these results and to show how they relate to one another in achieving an effective overall analysis procedure for analyzing a hyperspectral image data set.

Paper Details

Date Published: 31 October 1997
PDF: 11 pages
Proc. SPIE 3118, Imaging Spectrometry III, (31 October 1997); doi: 10.1117/12.278934
Show Author Affiliations
David A. Landgrebe, Purdue Univ. (United States)


Published in SPIE Proceedings Vol. 3118:
Imaging Spectrometry III
Michael R. Descour; Sylvia S. Shen, Editor(s)

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