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Remote Sensing Technology and Application
    
Improved HMRF for Remote Sensing Image Segmentation
Yang Jun1,Pei Jianjie2
(1.School of Electronic and Information Engineering,Lanzhou Jiaotong University,Lanzhou 730070,China;2.Faculty of Geomatics,Lanzhou Jiaotong University &Gansu Provincial Engineering Laboratory for National Geographic State Monitoring,Lanzhou 730070,China )
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Abstract  The traditional Markov random field algorithm used for image segmentation is often associated with some known problems,such as unsmooth edges of the segmented regions due toimage noise and abnormal pixels values,thus,subsequently inaccuracy segmentation results.On account of this phenomenon,an algorithm that follows the hidden Markov random field which is based on finite Gaussian mixture model is put forward.First,the initial segmentation results are obtained by replacing traditional K-means method with the Expectation Maximization (EM) algorithm,and they are smoothedby using the bilateral filter.Next,the finite Gaussian mixture model and the Potts modelare used to model the feature field and the mark field,and the EM algorithm is used for its parameter estimationto obtain the feature field energy and the mark field energy.Finally,the energy function is minimized by using the Iterative Condition Model (ICM) algorithm in order to achievean optimal segmentation result.Experimental results show that our approach achieved a more efficient result by comparingto the classical MRF method and the traditional HMRF method,and the probabilistic rand index and global consistency error indicators are better than that of existing
Key words:  Bayesian theorem      Hidden Markov random field      Gaussian mixture model      Bilateral Filter      Expectation maximization     
Received:  02 December 2017     
ZTFLH:  TP237  
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Yang Jun, Pei Jianjie. Improved HMRF for Remote Sensing Image Segmentation. Remote Sensing Technology and Application, 2018, 33(5): 857-865.

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http://www.rsta.ac.cn/EN/10.11873/j.issn.1004-0323.2018.5.0857     OR     http://www.rsta.ac.cn/EN/Y2018/V33/I5/857

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