摘要
提出一种基于贪心EM算法的HMRF遥感影像变化检测算法。该算法采取PCA与差值法相结合的方式来构造差分影像。首先,采用隐马尔可夫随机场(Hidden Markov Random Field,HMRF)模型描述空间上下文信息,并构造系统能量函数;然后,利用贪心EM算法克服EM算法假定混合成分数为已知、迭代结果过分依赖初始值、可能收敛到局部最大点或收敛到参数空间边界的缺点,能够准确学习分布模型结构和参数,发现数据对模型的最佳匹配;最后,通过条件迭代模型(Iterated Conditional Modes,ICM)优化算法求解能量函数最优解,获取变化区域。实验结果表明,该算法能够更好地保持影像的结构性,有效去除孤立噪声。
A remote sensing image change detection approach based on greedy Expectation Maximization （EM） algorithm for Hidden Markov Random Field （HMRF） is proposed. The difference image is constructed by Principal Component Analysis （PCA） and subtraction operation. Firstly, the HMRF model is applied to characterize the contexture-dependent information, and the energy function of system is defined. Secondly, the greedy EM algorithm is used to overcome the disadvantage of the standard EM algorithm that assumed the number of the mixture components is a known priori, the performance of the overall parameter estimation process depends on the given good initial settings excessively, and the estimated parameter can be resulted from some local optimum points. The distribution model structure and parameters are learned accurately to find the best fit of the given data. Finally, the changed area is obtained by using Iterated Conditional Modes （ICM） to optimize the energy function. Experiments show that the proposed method has virtues of preserving structural change and filtering noises.
出处
《光电工程》
CAS
CSCD
北大核心
2011年第11期50-56,共7页
Opto-Electronic Engineering
基金
自动化部队科学研究项目(2S100402)
陕西省自然科学基金资助项目(2010HM8038)
