Classification of satellite images using the cellular automata approach

Abstract

Nowadays, remote sensing allows us the acquisition of information using techniques that do not require be in contact with the object or area being observed. This science can be used in many environmental applications, helping to solve and improve the social problems derived from them. Examples of remotely sensed applications are in soil quality, water resources, environmental management and protection or meteorology, among others. The classification algorithms are one of the most important techniques used in remote sensing that help developers to interpret the information contained in the satellite images. At present, there are several classification processes, i.e., maximum likelihood, paralelepiped or minimum distance classifier, among others. In this paper, we investigate a new Classification Algorithm based on Cellular Automata (ACA): a technique usually used by researchers on Complex Systems. This kind of classifier will be validated and experimented in the SOLERES framework.