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Proceedings Paper

Wavelet transform as a preprocessing step for classifying AVIRIS scenes
Author(s): Thomas S. Moon; Erzsebet Merenyi
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Paper Abstract

This paper presents the results of ongoing research aimed at reducing the size of a hyperspectral data set without significant loss in information prior to classifying a scene. We are using an atmospherically corrected 614 by 397 pixel subset of a 1994 AVIRIS image of the Lunar Crater Volcanic Field, where a great diversity of cover types can be found. An artificial neural network (ANN) has already ben sued to distinguish over twenty different surface units some of which exhibit very subtle spectral differences. This ANN classification utilized the entire 224 spectral bands obtained at each pixel. We test the hypothesis that a discrete wavelet transform of these spectral data vectors can be used to reduce their length prior to classifying the scene. This is possible because the spectra can be relatively sparse in the wavelet domain after removal of the smallest wavelet components. Since the transform is linear, spectral information is preserved and pixel classification can be based on the smaller data vectors. An ANN is being used as a sensitive tool to test this hypothesis and determine relative loss of information due to the wavelet compression. A substantial amount of ground truth from past extensive research by us and others is also being used in support of our analysis. If successful, wavelet compression could significantly increase the efficiency of a classification.

Paper Details

Date Published: 4 August 1997
PDF: 9 pages
Proc. SPIE 3071, Algorithms for Multispectral and Hyperspectral Imagery III, (4 August 1997); doi: 10.1117/12.280601
Show Author Affiliations
Thomas S. Moon, Montana Tech of the Univ. of Montana (United States)
Erzsebet Merenyi, Univ. of Arizona (United States)


Published in SPIE Proceedings Vol. 3071:
Algorithms for Multispectral and Hyperspectral Imagery III
A. Evan Iverson; Sylvia S. Shen, Editor(s)

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