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Study on Terrain Classification Based on MRF Model and Statistical Modeling of SAR Imagery

Author: SunShuJin
Tutor: ZouHuanXin
School: National University of Defense Science and Technology
Course: Information and Communication Engineering
Keywords: Synthetic Aperture Radar Markov Random Field Statistical modeling Image Classification Connection function
CLC: TN957.52
Type: Master's thesis
Year: 2011
Downloads: 60
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Abstract


Synthetic aperture radar (Synthetic Aperture Radar, SAR) as an information to obtain the means , the prominent strategic significance in the defense , environment and other aspects . SAR Image Classification SAR image interpretation and take full advantage of the context information and the image gray image based on Markov Random Field Airport (Markov Random Field, MRF) and statistical modeling of SAR Image Classification the statistical distribution , and widely used in image processing . This paper systematically studies based MRF and statistical modeling , SAR Image Classification . First , as the theoretical basis of the full text of the study , in-depth analysis of the statistical model of the SAR image clutter . Comprehensive overview of existing SAR image clutter statistical model , in-depth study of the parameter estimation method based on the Mellin transform , summed up the parameter estimation method of the parameters in the existing distribution model to solve the equation . In order to meet the demand for data modeling under high-resolution conditions , the paper focuses on the research and analysis based dictionary set SAR image statistical modeling methods ( DSEM ) . Study in order to complete the multi-polarization SAR data modeling , multi channel data modeling tool : copula function is connected . Second, the study of unipolar SAR image classification method based MRF model and statistical modeling . Study the theoretical framework of the MRF model is applied to image classification , and combined with the basic knowledge of the statistical modeling of SAR image , SAR image classification method and the optimization algorithm of solving the optimal labeling matrix . Based on copulas theory proposes a first establish the joint distribution of the gray-scale data space and the contrast of texture data space , and then completed unipolar image classification method based on the MRF model . Finally, the study of multi-polarization SAR image classification method based on the MRF model and statistical modeling . In this study , the idea of multi-polarization SAR image statistical modeling method based on copulas function , and learn from the dictionary set a mixed copulas modeling method based on dictionary set . The experiment confirmed that the multi-polarization SAR image statistical distribution modeling , the method is a better description of the data capacity . Also verified and compared with the the unipolar SAR image classification , multi-polarization SAR image classification due to the comprehensive utilization of multi-channel data , it is possible to achieve better classification results .

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CLC: > Industrial Technology > Radio electronics, telecommunications technology > Radar > Radar equipment,radar > Radar receiving equipment > Data,image processing and admission
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