Abstract：Gaussian mixture model (GMM) is used to describe the probability density function of remote sensing data. According to the process of parameter estimation and the calculation of posterior probability,remote sensing information extraction can be realized. For the purpose of improving the accuracy of the extraction by GMM, Markov Random Field (MRF) is applied to calculate the prior probability of each feature in the pixel’s neighborhood to replace the mixing probability of the feature, and spatial correlation is reflected by this way. Then simulated annealing (SA) is utilized for the acquisition of overall optimum estimation of parameters. With the parameters, posteriori probability for every feature of each pixel is computed and the distribution of features is obtained. Extracting information from the images obtained from the remote sensing test site reveals that the new method has a better performance, thus proving the effectiveness of the above-mentioned improvements.
胡波, 朱谷昌, 张远飞, 冷超. 基于高斯混合模型的遥感信息提取方法研究[J]. 国土资源遥感, 2012, 24(4): 41-47.
HU Bo, ZHU Gu-chang, ZHANG Yuan-fei, LENG Chao. Method for Extraction of Remote Sensing Information Based on Gaussian Mixture Model. REMOTE SENSING FOR LAND & RESOURCES, 2012, 24(4): 41-47.
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2012, Vol. 24 
