摘要
为了准确建立高分辨率遥感影像同质区域内像素强度呈现的非对称、重尾和多峰等复杂分布的统计模型,并得到高精度的分割结果,提出了一种结合层次化高斯混合模型(HGMM)和Metropolis-Hastings (M-H)算法的高分辨率遥感影像分割算法.提出算法采用HGMM建立高分辨率遥感影像的复杂统计分布模型,HGMM由多个高斯子混合模型(GSMM)的加权和定义.根据贝叶斯理论,结合HGMM和参数先验分布构建后验分布,即分割模型.采用M-H算法模拟分割模型以实现影像分割和模型参数求解.为了验证提出算法的可行性和有效性,分别对合成影像和高分辨率遥感影像进行分割实验,并定量和定性地评价分割结果.结果表明:提出算法具有准确建立复杂统计分布模型的能力,并得到高精度的分割结果.
To accurately model the complicated statistical distribution of pixel intensities with asymmetry, heavy-tailed and multimodal characteristics in a homogeneous region of high resolution remote sensing image, a high resolution remote sensing image segmentation algorithm combining hierarchical Gaussian mixture model(HGMM) with Metropolis-Hastings(M-H) algorithm was proposed. The HGMM, which was defined by the weighted sum of several Gaussian sub-mixture models(GSMM), was used to build the complicated statistical distribution model for high resolution remote sensing images. Following Bayesian theory, posterior distribution, namely the segmentation model was built by combining HGMM with the prior distributions of parameters. M-H algorithm was designed to simulate the segmentation model to segment image and estimate parameters. To verify the feasibility and effectiveness of the proposed algorithm, segmentation experiments on synthetic and high resolution remote sensing images were carried out. The experimental results were analyzed quantitatively and qualitatively. The results show that the proposed algorithm can accurately model the complicated distribution, and obtain results of high precision.
作者
石雪
李玉
赵泉华
SHI Xue;LI Yu;ZHAO Quanhua(Institute for Remote Sensing,School of Geomatics,Liaoning Technical University,Fuxin,Liaoning 123000,China)
出处
《中国矿业大学学报》
EI
CAS
CSCD
北大核心
2019年第3期668-675,共8页
Journal of China University of Mining & Technology
基金
国家自然科学基金项目(41301479
41271435)
辽宁省自然科学基金项目(2015020090)
关键词
遥感影像分割
贝叶斯理论
层次化高斯混合模型
高斯子混合模型
M-H算法
remote sensing image segmentation
Bayesian theory
hierarchical Gaussian mixture model(HGMM)
Gaussian sub-mixture model(GSMM)
M-H algorithm
