Unsupervised Segmentation of Hyperspectral Images through Evolved Cellular Automata

The problem of segmenting multidimensional images, in particular hyperspectral images, is still an open subject. The main issue is related to preserving the multidimensional character of the signals throughout the segmentation process avoiding an early projection onto a 2D plane with the consequent loss of the wealth of information these images provide. The approach followed here is based on the use of cellular automata (CA) and their emergent behavior over the hyperspectral cube in order to achieve this objective. Using cellular automata for segmentation in hyperspectral images is not new, but most approaches to this problem involve hand designing the rules for the automata. Additionally, most references found are just extensions of one or three-dimensional methods to multidimensional images, and, as a consequence, average out the spectral information present. The main contributions of this paper is the study of the application of evolutionary methods to produce the CA rule sets that result in the best possible segmentation properties under different circumstances without resorting to any form of projection until the information is presented to the user. The procedure has been tested over synthetic and real hyperspectral images.