| Technology and Methodology |
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Method for Extraction of Remote Sensing Information Based on Gaussian Mixture Model |
| HU Bo1,2,3, ZHU Gu-chang3, ZHANG Yuan-fei4, LENG Chao5 |
1. Peking University, Beijing 100871, China;
2. Central South University, Changsha 410083, China;
3. Sinotech Minerals Exploration Co., Ltd., Beijing 100012, China;
4. China Non-ferrous Metals Resource Geological Survey, Beijing 100012, China;
5. CNPC Bureau of Geophysical Prospecting, Zhuozhou 072751, China |
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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.
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| Keywords
Qinghai-Tibet Plateau uplift
geological environment
ecological environment
response
power source
catalyst
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Issue Date: 13 November 2012
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| [1] 雷建军,杨震,刘刚,等.基于复高斯混合模型的鲁棒VAD算法[J].天津大学学报,2009,42(4):353-356.Lei J J,Yang Z,Liu G,et al.Robust Voice Activity Detection Algorithm Based on Complex Gaussian Mixture Model[J].Journal of Tianjin University,2009,42(4):353-356(in Chinese with English Abstract).[2] Stauffer C,Grimson W E L.Adaptive Background Mixture Models for Real-time Tracking[C].ICCV,1999:246-252.[3] 余鹏,封举富,童行伟.一种新的基于高斯混合模型的纹理图像分割方法[J].武汉大学学报:信息科学版,2005(6):514-517.Yu P,Feng J F,Tong X W.A New Textured Image Segmentation Algorithm Based on Gaussian Mixture Models[J].Editorial Board of Geomatics and Information Science of Wuhan University,2005(6):514-517(in Chinese with English Abstract).[4] Besag J.On the Statistical Analysis of Dirty Pictures[J].Journal of the Royal Statistical Society,Series B(Methodological),1986,48(3):259-302.[5] Dempster A P,Laird N M,Rubin D B.Maximum Likelihood for Incomplete Data Via the EM Algorithm[J].Journal of the Royal Statistical Society,Serious B(Methodological),1977,39:1-38.[6] Mclanchlan G J.The EM Algorithm and Extension[M].New York:Wily & Sons,1997.[7] Sanjay G S,Thomas J H.Bayesian Pixel Classification Using Spatially Variant Finite Mixtures and the Generalized EM Algorithm[J].IEEE Transactions on Image Processing,1998,7(7):1014-1028.[8] Hammersley J M,Cliford P.Markov Field on Finite Graphs and Lattices[Z].Unpublished manuscript,1971.[9] Peter J M L,Emile H L A.Simulated Annealing:Theory and Applications[M] Dordrecht,Holland:ReidelPub,1987.[10] Geman S,Gemini D.Stochastic Relaxation,Gibes Distributions,and the Bayesian Restoration of Images[J].IEEE Transactions on Pattern Analysis and Machine Intelligence,1984,6(6):721-741.[11] Landgrebe D.Multispectral Data Analysis:A Signal Theory Perspective[R].West Lafayette:Purdue University,1998. |
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2012,
Vol. 24
