高斯Copula的多光谱遥感影像分割  

Multispectral Remote Sensing Image Segmentation Using Gaussian Copula

作　　者：赵泉华[1] 赵静[1] 张洪云[1] 李玉[1] ZHAO Quanhua;ZHAO Jing;ZHANG Hongyun;LI Yu(Institute for Remote Sensing Science and Application, School of Mapping and Geographical Science, Liaoning Technical University, Fuxin 123000)

机构地区：[1]辽宁工程技术大学测绘与地理科学学院遥感科学与应用研究院

出　　处：《模式识别与人工智能》2019年第7期633-641,共9页Pattern Recognition and Artificial Intelligence

基　　金：国家自然科学青年科学基金项目(No.41301479);辽宁省高校创新人才支持计划项目(No.LR2016061);辽宁省教育厅科学技术研究一般项目(No.LJCL009)资助~~

摘　　要：为了充分利用多光谱影像波段间的相关性,提出高斯Copula的多光谱遥感影像分割方法.首先,建立基于马尔可夫随机场的标号场模型,使用Potts模型刻画该标号场.然后,建立表征像素光谱测度的特征场,利用高斯Copula建立像素光谱测度的多变量统计模型以刻画该特征场.结合标号场、特征场模型及各模型参数的先验概率,利用贝叶斯定理建立多光谱影像分割的后验概率模型.最后,设计适用于模拟后验概率模型的M-H算法,在最大后验概率策略下获取最优分割结果.对模拟和真实多光谱影像分割结果表明,文中方法描述波段间相关性的能力较强,准确性较高.To take full advantage of inter-band correlations of multispectral remote sensing images, a multispectral remote sensing image segmentation method based on Gaussian copula function is proposed. Firstly, the Markov random field model is exploited to establish a label field and the label field is characterized by the Potts model. Then, the feature field characterizing pixel spectral measurements is built. A multivariate statistical model based on Gaussian copula modeling pixel spectral measurement is proposed. Furthermore, a posterior probability model of multispectral remote sensing image segmentation is established by Bayes theorem combined with the label field model, the feature field model and the prior probabilities of model parameters. The Metropolis-Hastings algorithm is designed to simulate the posterior probability model, and the optimal segmentation is obtained under the maximum a posterior strategy. Experiments are carried out with simulated and real multispectral images respectively, and experimental results indicate that the proposed algorithm has a strong ability to describe the correlation between bands with a high accuracy.

关 键 词：高斯Copula 遥感影像分割 马尔可夫随机场(MRF) Metropolis-Hastings(M-H)算法 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]