X. Lun

Publications (1)0.97 Total impact

• Article: Mixture model and Markov random field-based remote sensing image unsupervised clustering method

  Y. Hou, Y. Yang, N. Rao, X. Lun, J. Lan
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  ABSTRACT: In this paper, a novel method for remote sensing image clustering based on mixture model and Markov random field (MRF) is proposed. A remote sensing image can be considered as Gaussian mixture model. The image clustering result corresponding to the image label field is a MRF. So, the image clustering procedure is transformed to a maximum a posterior (MAP) problem by Bayesian theorem. The intensity difference and the spatial distance between the two pixels in the same clique are introduced into the traditional MRF potential function. The iterative conditional model (ICM) is employed to find the solution of MAP. We use the max entropy criterion to choose the optimal clustering number. In the experiments, the method is compared with the traditional MRF clustering method using ICM and simulated annealing (SA). The results show that this method is better than the traditional MRF model both in noise filtering and miss-classification ratio. Keywordsremote sensing image–Markov random field–maximum a posteriori–iterative conditional model
  Opto-Electronics Review 04/2012; 19(1):83-88. · 0.97 Impact Factor