通信学报2017，Vol.38Issue(2)：34-43,10.DOI:10.11959/j.issn.1000-436x.2017026
可变类空间约束高斯混合模型遥感图像分割
Remote sensing image segmentation based on spatially constrained Gaussian mixture model with unknown class number
摘要
Abstract
In view of the traditional Gaussian mixture model (GMM), it was difficult to obtain the number of classes and sensitive to the noise. A remote sensing image segmentation method based on spatially constrained GMM with unknown number of classes was proposed. First, in the built GMM, prior probability that represented the membership between a pixel and one class was modeled as a Markov random field (MRF). In order to improve the sensitivity of noise, the smoothing factor was defined by combining the a posterior probability and the prior probability of neighboring pixels. For estimating the number of classes and the parameters of model, the reversible jump Markov chain Monte Carlo (RJMCMC) and maximum likelihood (ML) estimation were employed, respectively. Finally, by minimizing the smooth-ing factor the final segmentation was obtained. In order to verify the proposed segmentation method, the synthetic and real panchromatic images were tested. The experimental results show that the proposed method is feasible and effective.关键词
高斯混合模型/空间约束/最大似然估计/可逆跳变马尔可夫链蒙特卡罗/遥感图像分割Key words
Gaussian mixture model (GMM)/spatially constrained/maximum likelihood (ML)/reversible jump Markov chain Monte Carlo (RJMCMC)/remote sensing image segmentation分类
信息技术与安全科学引用本文复制引用
赵泉华,石雪,王玉,李玉..可变类空间约束高斯混合模型遥感图像分割[J].通信学报,2017,38(2):34-43,10.基金项目
国家自然科学基金资助项目(No.41301479, No.41271435) （No.41301479, No.41271435）
辽宁省自然科学基金资助项目(No.2015020090) The National Natural Science Foundation of China (No.41301479, No.41271435), The Natural Science Founda-tion of Liaoning Province (No.2015020090) （No.2015020090）
