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Proceedings Paper

New approach for hyperspectral mineral exploitation
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Paper Abstract

This paper's objective is to present a new, computationally efficient method for automatic exploration, detection and recognition. The automatic mineral homogeneous region separation algorithm developed by A.U.G. Signals in cooperation with the Canadian Space Agency (CSA) using AVIRIS data and mineral signatures from the Nevada's (U.S.) Cuprite site is described. The hyperspectral data and spectral signatures were provided by the Canada Center for Remote Sensing (CCRS). The algorithm is able to successfully divide the image in regions where the mineral composition remains constant. Hence, it can be used for reducing the noise is estimating the abundance parameters of the minerals on a pixel-by-pixel basis, for image region selection and hyperspectral image labeling for data storage and/or selective transmission. This may be another form of lossless hyperspectral image compression. Through the presented approach we are able to: a) divide a hyperspectral image into regions of adaptivity where pixel unmixing algorithms are able to extract the abundance parameters with higher degree of confidence, b) increase the signal to noise ration (SNR) of the present spectral signatures in a region and c) apply the proposed hyperspectral homogeneous region separation for data reduction (hyperspectral image compression). Experimental and theoretical results and comparisons/tradeoff studies are presented.

Paper Details

Date Published: 17 January 2002
PDF: 10 pages
Proc. SPIE 4480, Imaging Spectrometry VII, (17 January 2002); doi: 10.1117/12.453366
Show Author Affiliations
George A. Lampropoulos, A.U.G. Signals Ltd. (Canada)
Yifeng Li, A.U.G. Signals Ltd. (Canada)
Shen-en Qian, Canadian Space Agency (Canada)
Allan Bernard Hollinger, Canadian Space Agency (Canada)

Published in SPIE Proceedings Vol. 4480:
Imaging Spectrometry VII
Michael R. Descour; Sylvia S. Shen, Editor(s)

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