Journal
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
Volume 40, Issue 10, Pages 2331-2338Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TGRS.2002.804721
Keywords
feature extraction; hyperspectral; remote sensing; target detection; wavelet decomposition
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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.
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