Dimensionality reduction of hyperspectral data using discrete wavelet transform feature extraction

• 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
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2002.804721

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.