Remote Sensing Image Segmentation Based on SVM Posterior Probability and Improved Multi-scale MRF

Current image segmentation methods have two problems in Bayesian frameaork,one is that the estimation of probability distribution for the observed filed is not accurate,the other is that making use of the relevant information of tag field is not enough.This article overcomes the problem of inaccurately modeling the observed field by Gauss matured model,through the approach which estimates the likelihood probability by SVM method.Meanwhile,this article makes full use of the statistical characteristic of tag field through the improved multi-scale MRF model which takes full account of the correlation within various scales and between the same scale.Finally,the proposed image segmentation approach uses the improved modeling approach under the framework of sequential maximum a posteriori probability estimation algorithm(SMAP).The proposed method is proved to improve the accuracy of segmentation through the experimental results of the artificial synthetic and real remote sensing image.