A hybrid edge detection model of extreme learning machine and cellular automata

Abstract

For remote sensing image, whose spectral signatures are intricate, the traditional edge detection methods cannot obtain satisfactory results. This paper takes the space computing capacity of Cellular Automata (CA) and the data pattern search ability of Extreme Learning Machine (ELM) into account and puts forward a new hybrid edge detection model based on Extreme Learning Machine and Cellular Automata (ELM-CA) for remotely sensed imagery. This model can extract evolution rules of cellular automata. On the basis of the rules, false edges are removed and purer edge map is obtained. The result of the simulation experiment shows that the performance of method suggested by this paper is much better compared to other edge detection arithmetic operators. It can prove that ELM-CA is an ideal method of remote sensing image edge detection.