Segmentation method for remote sensing image based on cloud model, graph theory and mutual information

The traditional segmentation method which is based on local information search technique gives little regard for the global information of the image and ignores the randomness and uncertainty of image segmentation. In view of this, this paper proposes a new segmentation method which is based on cloud model, graph theory and mutual information. Firstly, we could use the cloud model to reflect the uncertainty and randomness when pixel cluster into regions. Secondly, when the graph theory method is introduced into a quasi-optimal cut sets, we could obtain a globally optimal segmentation. Thirdly, by using the multidimensional characteristics which are showed by regional concept of cloud model, we could use a comprehensive heterogeneity measure to improve border weights, and therefore improve the ability to distinguish regional dissimilarity. From the experimental results, the proposed method can produce meaningful, complete and internal-homogeneity divided region, moreover, the segmentation accuracy can meet the basic human visual requirements. ©, 2015, Chinese Institute of Electronics. All right reserved.