Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction

In this paper, the dyadic discrete wavelet transform is proposed for feature extraction from a high-dimensional data space. The wavelet's inherent multiresolutional properties are discussed in terms related to multispectral and hyperspectral remote sensing. Furthermore, various wavelet-based fe...

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Bibliographic Details
Published in:IEEE transactions on geoscience and remote sensing 2002-10, Vol.40 (10), p.2331-2338
Main Authors: Bruce, L.M., Koger, C.H., Jiang Li
Format: Article
Language:English
Subjects:
Applied geophysics
Automated
Classification
Data analysis
Discrete Wavelet Transform
Discrete wavelet transforms
Earth sciences
Earth, ocean, space
Exact sciences and technology
Feature extraction
Grounds
Hyperspectral imaging
Hyperspectral sensors
Internal geophysics
Remote sensing
Signal resolution
Signatures
Training data
Wavelet analysis
Wavelet transforms
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Summary:In this paper, the dyadic discrete wavelet transform is proposed for feature extraction from a high-dimensional data space. The wavelet's inherent multiresolutional properties are discussed in terms related to multispectral and hyperspectral remote sensing. Furthermore, various wavelet-based features are applied to the problem of automatic classification of specific ground vegetations from hyperspectral signatures. The wavelet transform features are evaluated using an automated statistical classifier. The system is tested using hyperspectral data for various agricultural applications. The experimental results demonstrate the promising discriminant capability of the wavelet-based features. The automated classification system consistently provides over 95% and 80% classification accuracy for endmember and mixed-signature applications, respectively. When compared to conventional feature extraction methods, the wavelet transform approach is shown to significantly increase the overall classification accuracy.
ISSN:0196-2892
1558-0644
DOI:10.1109/TGRS.2002.804721