Evolving cellular automata for detecting edges in hyperspectral images

This paper deals with the problem of segmenting or, more properly, finding edges in multidimensional images, in particular, hyperspectral images. The approach followed is based on the use of cellular automata (CA) and their emergent behavior in order to achieve this objective. Using cellular automata for finding edges in hyperspectral images is not new, but most current approaches to this problem involve hand designing the rules for the automata. On the other hand, many authors just use extensions of one-dimensional edge detection methods to multidimensional images, thus averaging out the spectral information present. Here, we consider the application of evolutionary methods to produce the CA rule sets that obtain the best possible edge detection properties under different circumstances and using spectral based approaches. The procedure has been tested over synthetic and real hyperspectral images and the results obtained have been compared to those produced using the hyper-Sobel and Hyper-Prewitt operators, which are standard edge detection methods for gray-level images that have been extended by some authors to the multidimensional domain.