In order to detect a target or anomaly in a hyper-spectral image the classes associated with the background have to be identified. We propose a computationally efficient methodology to determine the background classes present in the image. The method is based on the assumption that mixed and anomaly pixels are relatively rare in comparison with the abundance of the background class pixels. The method considers the background classes as groups of distinct measurements and consists of robust clustering of a randomly picked small percentage of the image pixels. The resulting clusters may be considered as representatives of the background of the image. Several clustering techniques are investigated and experimental results using hyperspectral data are presented. The proposed technique using a self-organising map is then compared with a state-of the art endmember extraction technique.
['Olga Duran', 'Maria Petrou']
A time-efficient clustering method for pure class selection