An Extreme Learning Machine based on Cellular Automata of edge detection for remote sensing images

For remote sensing images, 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 an Extreme Learning Machine based on Cellular Automata (ELM-CA) of edge detection for remote sensing images. 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 the 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.