Fast feature extraction in hyperspectral imagery via lifting wavelet transforms

James F. Scholl; Eustace L. Dereniak; E. Keith Hege

doi:10.1117/12.613327

16 September 2005 Fast feature extraction in hyperspectral imagery via lifting wavelet transforms

James F. Scholl, Eustace L. Dereniak, E. Keith Hege

Proceedings Volume 5915, Mathematics of Data/Image Coding, Compression, and Encryption VIII, with Applications; 59150N (2005) https://doi.org/10.1117/12.613327
Event: Optics and Photonics 2005, 2005, San Diego, California, United States

Abstract

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.

Citation Download Citation

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); https://doi.org/10.1117/12.613327

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