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Please use this identifier to cite or link to this item: http://hdl.handle.net/10204/4062

Title: Using random matrix theory to determine the number of endmembers in a hyperspectral image
Authors: Cawse, K
Sears, M
Robin, A
Damelin, SB
Wessels, K
Van den Bergh, F
Mathieu, R
Keywords: Hyperspectral unmixing
Random matrix theory
Linear mixture model
Virtual dimension
Signal processing
Remote sensing
Issue Date: Jun-2010
Citation: Cawse, K, Sears, M, Robin, A et al. 2010. Using random matrix theory to determine the number of endmembers in a hyperspectral image. The 2nd Workshop in Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). 14-16 June 2010, Reykjavik, Iceland, pp 4
Abstract: Determining the number of spectral endmembers in a hyperspectral image is an important step in the spectral unmixing process, and under- or overestimation of this number may lead to incorrect unmixing for unsupervised methods. In this paper we discuss a new method for determining the number of endmembers, using recent advances in Random Matrix Theory. This method is entirely unsupervised and is computationally cheaper than other existing methods. We apply our method to synthetic images, including a standard test image developed by Chein-I Chang, with good results for Gaussian independent noise
Description: The 2nd Workshop in Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). 14-16 June 2010, Reykjavik, Iceland
URI: http://hdl.handle.net/10204/4062
Appears in Collections:Earth observation
Advanced mathematical modelling and simulation
General science, engineering & technology

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