Fast wavelet based feature extraction of spatial and spectral information from hyperspectral datacubes

James F. Scholl; Eustace L. Dereniak

doi:10.1117/12.557931

15 October 2004 Fast wavelet based feature extraction of spatial and spectral information from hyperspectral datacubes

Proceedings Volume 5546, Imaging Spectrometry X; (2004) https://doi.org/10.1117/12.557931
Event: Optical Science and Technology, the SPIE 49th Annual Meeting, 2004, Denver, Colorado, United States

Abstract

An ongoing problem for feature extraction in hyperspectral imagery is that such data consumes large amounts of memory and transmittance bandwidth. In many applications, especially on space based platforms, fast, low power feature extraction algorithms are necessary, but not feasible. To overcome many of the problems due to the large volume of hyperspectral data we have developed a fast, low complexity feature extraction algorithm that is a combination of a fast integer-valued hyperspectral discrete wavelet transform (HSDWT) using a specialized implementation of the Haar basis and an improved implementation of linear spectral unmixing. The Haar wavelet transform implementation involves a simple weighted sum and a weighted difference between pairs of numbers. Features are found by using a small subset of the transform coefficients. More refined spatial and/or spectral identifications can then be made by localized fast inverse Haar transforms using very small numbers of additional coefficients in the spatial or spectral directions. The computational overhead is reduced further since much of the information used for linear spectral unmixing is precomputed and can be stored using a very small amount of additional memory.

Citation Download Citation

James F. Scholl and Eustace L. Dereniak "Fast wavelet based feature extraction of spatial and spectral information from hyperspectral datacubes", Proc. SPIE 5546, Imaging Spectrometry X, (15 October 2004); https://doi.org/10.1117/12.557931

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