This paper proposes an intrinsically distributed cellular automata (CA) based approach to address the perennial problem of real time segmentation and classification of high dimensional images, such as remote sensing hyperspectral images. This approach is efficiently implemented on GPUs providing results that improve on the state of the art algorithms presented in the literature. It is based on the evolutionary generation of the CA rule sets under two basic premises: During the segmentation process, the CAs must work over the whole dimensionality of the images without any projection onto lower dimensionalities, and the rule sets that are generated must be adapted to the segmentation level required by the user. The performance of the approach is tested over a benchmark set of well-known hyperspectral images and the results compared to the state of the art in the literature for two implementations, one using a SVM based classification stage and another that considers an ELM based classification stage.
Keywords: Hyperspectral image segmentation, cellular automata, evolutionary algorithm.