Using random matrix theory to determine the number of endmembers in a hyperspectral image

• Main Authors: Cawse, K, Sears, M, Robin, A, Damelin, SB, Wessels, Konrad J, Van den Bergh, F, Mathieu, Renaud SA
• Format: Conference Object
• Language: English
• Published: 2010
• Subjects: Hyperspectral unmixing; Random matrix theory; Linear mixture model; Virtual dimension; Signal processing; Remote sensing; Iceland
• Online Access: http://hdl.handle.net/10204/4062

The 2nd Workshop in Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS). 14-16 June 2010, Reykjavik, Iceland 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