4 February 2010 Wavelet packets for multi- and hyper-spectral imagery
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Abstract
State of the art dimension reduction and classification schemes in multi- and hyper-spectral imaging rely primarily on the information contained in the spectral component. To better capture the joint spatial and spectral data distribution we combine the Wavelet Packet Transform with the linear dimension reduction method of Principal Component Analysis. Each spectral band is decomposed by means of the Wavelet Packet Transform and we consider a joint entropy across all the spectral bands as a tool to exploit the spatial information. Dimension reduction is then applied to the Wavelet Packets coefficients. We present examples of this technique for hyper-spectral satellite imaging. We also investigate the role of various shrinkage techniques to model non-linearity in our approach.
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J. J. Benedetto, W. Czaja, M. Ehler, C. Flake, M. Hirn, "Wavelet packets for multi- and hyper-spectral imagery", Proc. SPIE 7535, Wavelet Applications in Industrial Processing VII, 753508 (4 February 2010); doi: 10.1117/12.843039; https://doi.org/10.1117/12.843039
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