Markov Random Field Based Fast Segmentation
LIU Wei qiang,CHEN Hong,XIA De shen()
In this paper,the segmentation based on Markov Random Field (MRF) is discussed to fulfill the fast segmentation of complex remote sensing image. Using this method, the cotton estimation model and the extraction of cotton areas from satellite image are realized and remote sensing cotton estimation system is constructed. According to the characteristics of the remote sensing image,the image segmentation model based on MRF is established.The problem of image segmentation can be converted to the problem of symbolizing,and finally converted to the solution of Maximum A Posterior (MAP), if the method of MRF is used. For obtaining the solution of MAP, the algorithm of simulated annealing (SA) can find the global optimum,but it requires a large amount of computation. So sub optimal algorithms are often used. In the article, the decisive algorithm based on game theory and the algorithm based on competition theory are both introduced. Moreover the competition algorithm(CA) is improved largely. These two algroithms reduce the complexity from different way. The experiments indicated that they could be used in the segmentation of complex remote sensing image effectively. In the system constructed by the method, the cotton areas are extracted with high precision from satellite image.