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 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.