Huang Ning, Zhu Minhui, Zhang Shourong. A remotely sensed image classification algorithm based on gaussian hidden markov random field model[J]. Journal of Electronics & Information Technology, 2003, 25(1): 50-53.
Citation:
Huang Ning, Zhu Minhui, Zhang Shourong. A remotely sensed image classification algorithm based on gaussian hidden markov random field model[J]. Journal of Electronics & Information Technology, 2003, 25(1): 50-53.
Huang Ning, Zhu Minhui, Zhang Shourong. A remotely sensed image classification algorithm based on gaussian hidden markov random field model[J]. Journal of Electronics & Information Technology, 2003, 25(1): 50-53.
Citation:
Huang Ning, Zhu Minhui, Zhang Shourong. A remotely sensed image classification algorithm based on gaussian hidden markov random field model[J]. Journal of Electronics & Information Technology, 2003, 25(1): 50-53.
The problem of unsupervised classification of remotely sensed image is considered in this paper. A Hidden Markov Random Field (HMRF) model is built and a new image clas-sification algorithm based on the HMRF model is presented to the remote sensing application. In the algorithm, the Finite Gaussian Mixture (FGM) model is used to describe the density function of the image pixel intensity, the Expectation Maximization (EM) algorithm is used for the HMRF model parameters under the incomplete data condition, and MAP (Maximum A Posteriori) method is used to estimate the image class label. As the MRF model with fixed parameters does not fit the real remotely sensed image very well, this paper adjusts the MRF models parameters during the classification procedure. The novel image classification method gives a more accurate and more robust image classification.
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