Research On Image Segmentation Based On Dynamic Fusion Of Multi-feature

With the rapid development of science and technology and the progress of social civilization, the exchange and processing of information is more and more important for people's daily life, the requirements of people for image processing is increased. Image segmentation is one of the most important technologies on image processing; it is the basis of image analysis and image understanding, so the image segmentation is still a hot research topic and problem. To solve the problems from image segmentation, in this paper, the aspects of this research is shown as follows:(1) There are a variety of features to describe the remote sensing images; common features are gray scale, texture, edge and so on. The paper first analyzes the advantages and disadvantages of various features, and the complementarities between the features are also studies, and then design a dynamic fusion model of multi-feature to improve the description of the remote sensing images, so as to make the follow segmentation more accurate.(2) The fusion of information can reflect the image properties better, so the paper proposed a novel sea-land segmentation algorithm based on dynamic fusion model of multi-feature. The method first extract texture and gray features of remote sensing images, and use them to describe the image properties, and then generate a map of integrated feature from this two features using the dynamic fusion model of multi-feature, so that it can accurately reflect image information, and thus guide the following segmentation, it can also improve the adaptability and robustness of segmentation algorithm.(3) The paper first analyzes the advantages and disadvantages of traditional region growing method, and then an improved method of region growing is proposed. The paper using k-means clustering method to solve the problem of selecting the initial seed for region growth difficultly, and use the integrated features which is generated by the dynamic fusion model of multi-feature to make the result of cluster more reasonable in the clustering process; at the same time, the integrated feature is also used as one of the region growth guidelines, and then it can reduce the impact of noise on the region growing method.