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16 September 2005 Fast feature extraction in hyperspectral imagery via lifting wavelet transforms
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Feature extraction from hyperspectral imagery consumes large amounts of memory. Hence the algorithms to do this have high computational complexity and require large amounts of additional computer memory. To address these issues previous work has concentrated on algorithms that are combinations of a fast integer-based hyperspectral discrete wavelet transform (HSDWT) with a specialized implementation of the Haar basis and improved implementations of linear spectral unmixing. Extensions of that previous work are presented here that modify and extend these algorithms to investigate feature extraction of arbitrary shaped spatial regions and incorporate more general biorthogonal bases for processing of spectral signatures. Finally, these wavelet transform implementations have also been used to simulate linear spectral unmixng techniques on spatially unresolved objects such as binary stars and globular star clusters.
© (2005) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
James F. Scholl, Eustace L. Dereniak, and E. Keith Hege "Fast feature extraction in hyperspectral imagery via lifting wavelet transforms", Proc. SPIE 5915, Mathematics of Data/Image Coding, Compression, and Encryption VIII, with Applications, 59150N (16 September 2005);

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