Paper
4 August 1997 Wavelet transform as a preprocessing step for classifying AVIRIS scenes
Thomas S. Moon, Erzsebet Merenyi
Author Affiliations +
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.
© (1997) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Thomas S. Moon and Erzsebet Merenyi "Wavelet transform as a preprocessing step for classifying AVIRIS scenes", Proc. SPIE 3071, Algorithms for Multispectral and Hyperspectral Imagery III, (4 August 1997); https://doi.org/10.1117/12.280601
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KEYWORDS
Wavelets

Discrete wavelet transforms

Reflectivity

Wavelet transforms

Artificial neural networks

Data compression

Image compression

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