Abstract. Spatial patterns in an image that shows a visual perception of roughness or softness of the surface is known as the texture of the image. Most of the analysis and description of texture found in the literature is based on statistical or structural properties of this attribute [2]. The field of cellular automata (CA), which has been developed mainly to model the dynamical behavior of systems, is based on the behavior or arrangements of pixel values and their neighborhood which, according to some rules behaves in different manners [2,8]. In this paper, within the frame of structural approach, a novel method based on the properties of linear cellular automata is proposed to characterize different sort of textures. To this purpose, it is assumed that a binary version of the image under study was generated by a cellular automata technique. By using this model a number of textural primitives are found which allows the production of a characterizing image. In order to verify the feasibility of the proposed method, texture images generated by CA techniques as well as natural images has been used.

Abstract. Nowadays, remote sensing is used in many environmental applications, helping to solve and improve the social problems derived from them. Examples of remotely sensed applications include soil quality studies, water resources searching, environmental protection or meteorology simulations. 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. In this paper we investigate a new satellite image classification Algorithm based on Cellular Automata (ACA), a technique usually used by researchers on complex systems. There are not previous works related to satellite image classification with cellular automata. This new kind of satellite image classifier, that improves the results obtained by classical algorithms in several aspects, has been validated and experimented in the SOLERES framework.

Remote sensing has been used in numerous environmental simulations with the aim of solving and improving many different kinds of problems, e.g., meteorology applications, soil quality studies, water resource exploration, and environmental protection. Besides, cellular automata have been widely used in the field of remote sensing for simulating natural phenomena over two-dimensional satellite images. However, simulations on Digital Elevation Models (DEM), or three-dimensional (3D) satellite images, are scarce. This paper presents a study of modeling and simulation of the weather phenomena of rainfall, water evaporation and groundwater flow in 3D satellite images through a new algorithm, developed by the authors, named RACA (RAinfall with Cellular Automata). The purpose of RACA is to obtain, from the simulation, numerical and 3D results related to the total cumulative flow and maximum level of water that allow us to make decisions on important issues such as analyzing how climate change will affect the water level in a particular area, estimating the future water supply of a population, establishing future construction projects and urban planning away from locations with high probability of flooding, or preventing the destruction of property and human life from future natural disasters in urban areas with probability of flooding.

No abstract

No abstract