Applying Cellular Automata-Based Structures to Hyperspectral Image Processing

Abstract

This chapter addresses the problem of processing hyperspectral images (HI) and sequences leading to high efficiency implementations. A new methodology based on the application of cellular automata (CA) is presented to solve two different processing tasks, the segmentation and denoising of HI and sequences, respectively. CA structures present potential benefits over traditional approaches since they are computationally efficient and can adapt to the particularities of the task to be solved. However, it is necessary to generate an appropriate rule set for each particular problem, which is usually a difficult task. The generation of the rule sets is handled here following a new methodology based on the application of evolutionary algorithms and using synthetic low-dimensionality images and sequences as training datasets, which results in CA structures that can be used to process HI and sequences successfully, thus avoiding the problem of lack of labeled reference images. Both processing approaches have been tested over real HI providing very competitive results.