一种改进的隐马尔可夫随机场遥感影像分割算法

Improved HMRF for Remote Sensing Image Segmentation

针对传统的马尔科夫随机场影像分割算法对影像噪声和像素异常值敏感,容易产生分割结果不准确以及边缘不平滑等问题,提出了一种基于有限高斯混合模型的隐马尔科夫随机场影像分割算法。首先,以期望最大化算法代替传统的K-means方法获得影像初始分割结果,并用双边滤波器对初始分割结果进行滤波处理。其次,使用有限高斯混合模型和Potts模型分别对影像特征场和标记场建模,并用期望最大化算法进行参数估计,从而获得特征场能量和标记场能量。最后,利用迭代条件模式算法进行能量函数最小化优化,获得最优分割结果。结果表明:相比于经典MRF方法和传统HMRF方法,该算法获得的分割结果更精确,并且概率兰德指数和全局一致性误差指标都优于这两种算法。

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 ab- normal 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 ap- proach achieved a more efficient result by comparingto the classical MRF method and the traditional HM- RF method,and the probabilistic rand index and global consistency error indicators are better than that of existing algorithms.