Computer Vision and Image Understanding
Geometric partial differential equations and image analysis
Geometric partial differential equations and image analysis
Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Integrated active contours for texture segmentation
IEEE Transactions on Image Processing
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As remote sensing images are multi-sensor, multi-spectral, multi-temporal phase and multi-resolution, general image segmentation methods always can not obtain satisfactory results. In this paper, we introduce the statistical region merging (SRM) model to segment remote sensing images. And according to the characteristics and defects of SRM, the model is improved as follows: Firstly, image gradient information is added in the sort function, which can increase the differences between regions; Secondly, we combine SRM with the nonlinear diffusion which can protect borders, then the requirements of regional homogeneity are better meted, and the model's anti-noise ability is also strengthened; Thirdly, for the issue of SRM has over merging defect, we give a predicate, by which the over merging regions are chosen and then segmented by IAC. Experiments on two color remote sensing images display the quality of the novel method.