自适应类别的层次高斯混合模型遥感影像分割

为了实现自动确定类别数的高精度遥感影像分割，提出一种自适应类别的层次高斯混合模型（Hierarchical Gaussian Mixture Model，HGMM）遥感影像分割算法.提出算法采用多个高斯混合模型加权和定义HGMM，用于建模具有非对称，重尾和多峰等复杂特性的影像统计模型.采用期望最大化算法（Expectation Maximization，EM）求解模型参数.为了实现自动确定类别数，采用贝叶斯信息准则（Bayesian Information Criterion，BIC）求解最优类别数，其中惩罚项采用加权像素数定义.为了验证提出算法可行性和有效性，对模拟和全色遥感影像进行分割实验，并对分割结果进行定性和定量分析.结果表明HGMM具有准确建模复杂统计分布的能力，提出算法具有高精度和高效率，同时可自动确定最优类别数.

Remote Sensing Image Segmentation Based on Hierarchy Gaussian Mixture Model with Self-adaptive Number of Classes

To accurately segment remote sensing image and automatically determine the number of classes,a Hierarchical Gaussian Mixture Model (HGMM) based remote sensing image segmentation algorithm with self-adaptive number of classes is proposed.In the proposed algorithm,HGMM is defined by the weighted sum of several Gaussian Mixture Models (GMM),which is used to model the asymmetric,heavy-tailed and multimodal distributions of image.Expectation Maximization (EM) is used to estimate the model parameters.Bayesian Information Criterion (BIC) is used to solve the optimal number of classes,where penalty term is defined by the weighted number of pixels.To test the feasibility and availability of the proposed algorithm,segmentation experiments are carried out on simulated and panchromatic remote sensing images.Segmentation results are analyzed qualitatively and quantitatively,which show that HGMM can accurately model the complicated statistical distributions.The proposed algorithm can obtain high accuracy and has good efficiency,as well as determine the optimal number of classes.