Hyperspectral feature classification with alternate wavelet transform representations

James F. Scholl; E. Keith Hege; Michael Lloyd-Hart; Eustace L. Dereniak

doi:10.1117/12.682583

25 August 2006 Hyperspectral feature classification with alternate wavelet transform representations

James F. Scholl, E. Keith Hege, Michael Lloyd-Hart, Eustace L. Dereniak

Proceedings Volume 6315, Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications IX; 63150G (2006) https://doi.org/10.1117/12.682583
Event: SPIE Optics + Photonics, 2006, San Diego, California, United States

Abstract

The effectiveness of many hyperspectral feature extraction algorithms involving classification (and linear spectral unmixing) are dependent on the use of spectral signature libraries. If two or more signatures are roughly similar to each other, these methods which use algorithms such as singular value decomposition (SVD) or least squares to identify the object will not work well. This especially goes for these procedures which are combined with three-dimensional discrete wavelet transforms, which replace the signature libraries with their corresponding lowpass wavelet transform coefficients. In order to address this issue, alternate ways of transforming these signature libraries using bandpass or highpass wavelet transform coefficients from either wavelet or Walsh (Haar wavelet packet) transforms in the spectral direction will be described. These alternate representations of the data emphasize differences between the signatures which lead to improved classification performance as compared to existing procedures.

Citation Download Citation

James F. Scholl, E. Keith Hege, Michael Lloyd-Hart, and Eustace L. Dereniak "Hyperspectral feature classification with alternate wavelet transform representations", Proc. SPIE 6315, Mathematics of Data/Image Pattern Recognition, Compression, and Encryption with Applications IX, 63150G (25 August 2006); https://doi.org/10.1117/12.682583

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