一种采用高斯隐马尔可夫随机场模型的遥感图像分类算法
A remotely sensed image classification algorithm based on gaussian hidden markov random field model
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摘要: 该文研究了无监督遥感图像分类问题。文中构造了图像的隐马尔可夫随机场模型(HiddenMarkov Random Fleid,HMRF),并且提出了基于该模型的图像分类算法。该文采用有限高斯混合模型(Finite Gaussian Mixture,FGM)描述图像像素灰度的条件概率分布,使用EM(Expectation-Maximization)算法解决从不完整数据中估计概率模型参数问题。针对遥感图像分布的不均匀特性,该文提出的算法没有采用固定的马尔可夫随机场模型参数,而是在递归分类算法中分级地调整模型参数以适应区域的变化。实验结果表明了该文算法的有效性,分类算法处理精度高于C-Means聚类算法.。Abstract: 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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Tulkel Derin, et al., Modeling and segmentation of noisy and textured images using Gibbs random fields, IEEE Trans. on PAMI., 1987, PAMI-9(1), 39-55.[2]T.N. Pappas, An adaptive clustering algorithm for image segmentation, IEEE Trans. on Signal Processing, 1992, SP-40(4), 901-913.[3]S.Z. Li, Markov Random Field Modeling in Computer Vision, Tokyo, Springer-Verlag, 1995.[4]Y.Y. Zhang, et al., Segmentation of Brain MR images through a hidden Markov random field model and the expectation maximization algorithm, IEEE Trans. on Medical Imaging, 2001,MI-20(1), 15-22. -
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