Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Texture Segmentation Using Markov Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov random field modeling in computer vision
Markov random field modeling in computer vision
Multiresolution Color Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Markov Random Field Models for Unsupervised Segmentation of Textured Color Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
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An unsupervised texture segmentation approach for multispectral remote-sensing images is proposed. Firstly, A SSF-based histogram thresholding is used to threshold each spectrum space of a multispectral remote-sensing image to detect the major clusters of the multispectral data to generate the principal multi-spectrum set. Secondly, a GMRF (Gaussian Markov random field) is used to model the multispectral texture image, then the global GMRF parameters in a posteriori distribution probability are estimated. We label each pixel of the image based on the principal multi-spectrum set and the global GMRF parameters to maximize a posteriori distribution probability (MAP). Thirdly, a uniformity criterion is presented to each pixel in the global segmented image to determine whether it should be estimated the local MRF parameters or not. A max-min distance clustering method is then used to cluster the estimated local MRF parameters to further segment the image. Several remote-sensing images were processed by the proposed approach to demonstrate the segmentation ability.