《SAR圖像統計建模:模型及套用》系統深入地論述了SAR圖像統計建模的理論、模型以及套用，以作者的科研工作為依託，較全面地反映了本學科的新近科技成果。《SAR圖像統計建模:模型及套用》內容涵蓋傳統單通道SAR圖像的統計建模和多通道干涉、極化等新體制雷達圖像的統計建模，並探討了新模型在目標檢測和地物分類等方面套用的算法。 全書共6章。第1章為sAR圖像統計建模的綜述；第2章介紹單通道sAR圖像的統計建模；第3章為單通道SAR圖像統計模型在目標檢測和地物分類的套用；第4章為多通道干涉SAR的統計建模；第5章為多通道干涉SAR統計模型在動目標檢測方面的套用；第6章介紹極化SAR圖像的統計建模及目標檢測方法。
基本介紹
- 書名：SAR圖像統計建模:模型及套用
- 作者：高貴 時公濤
- 出版日期：2013年8月1日
- 語種：簡體中文, 英語
- 品牌：國防工業出版社
- 外文名：Statistical Modeling of SAR Images: Models & Applications
- 出版社：國防工業出版社
- 頁數：185頁
- 開本：16
內容簡介,圖書目錄,
內容簡介
高貴編著的《SAR圖像統計建模--模型及套用(精)》系統深入地論述了SAR圖像統計建模的理論、模型以及套用，較全面地反映了本學科的新近科技成果。《SAR圖像統計建模--模型及套用(精)》適用於遙感圖像信息處理、雷達信號處理、圖像判讀專業的研究人員、工程技術人員、高等院校教師等閱讀參考，亦可作為高等院校遙感信息處理等有關專業的博士或碩士研究生課程教材或參考用書。
圖書目錄
Chapter 1 Overview for Statistical Modeling of SAR Images
1.1 Introduction
1.2 Model Classification and Research Contents
1.2.1 Parameter estimation
1.2.2 Goodness—of—fit tests
1.3 Statistical models
1.3.1 Nonparametric models
1.3.2 Parametric models
1.4 Classification of parametric models
1.4.1 The statistical models developed from the product model
1.4.2 The statistical models developed from the generalized central limit theorem
1.4.3 The empirical distributions
1.4.4 Other models
1.5 The Relationship Among The Major Models and Their Applications
1.5.1 The relationship among the parametric statistical models
1.5.2 Summary of the applications of the major models
1.6 Discussion of Future Work
1.7 Conclusions References
Chapter 2 Statistical Modeling of Single—Channel SAIl IInages
2.1 Modeling SAR Images Based on A Generalized Gamma Distribution for Texture Component
2.1.1 The Proposed GFFodel
2.1.2 Parameter Estimator of the GFF Model Based on MoLC
2.1.3 ExperimentaI Results
2.1.4 Appendix 2—A.The Derivation of m—th order moments of the Distribution
2.1.5 Appendix 2—B.Proof of the relationship between Distributions
2.2 An Empirical Distribution for Characterizing the Statistical Properties of SAR Clutter
2.2.1 The Proposed Distribution
2.2.2 The Parameter Estimators of The Proposed Distribution
2.2.3 Experimental Results
2. 2. 5 Appendix 2—C. The Derivation of m—th Order Moments of the Distribution
2. 2.6 Appendix 2—D. The Derivation of the Cumulative Distribution Function of HG
2. 3 An Improved Scheme for Parameter Estimation of G0 Distribution Model in High—Resolution SAR Images
2. 3.1 The G0 Model
2. 3. 2 MoM Based Parameter Estimation
2. 3. 3 MT Based Parameter Estimation
2. 3.4 Our Proposed Parameter Estimation
2. 3.5 Results and Discussion
2.4 Conclusions
References
Chapter 3 Target Detection and Terrain Classification of Single—Channel SAR Images
3. 1 A CFAR Detection Algorithm for Generalized Gamma Distributed Background in High—Resolution SAR Images
3. 1.1 Generalized Gamma Distribution and Its Estimation
3.1.2 CFAR Algorithm Using GFD for Background
3. 1.3 Performance Evaluation
3.2 A Parzen Window Kernel Based CFAR Algorithm for Ship Detection in SAR Images
3.2. 1 Statistical modeling of SAR image based on Parzen window kernel
3.2.2 CFAR detection
3. 2. 3 Experimental results
3. 3 A Markovian classification method for urban areas in High—resolution SAR images
3. 3. 1 Markovian Formalism
3. 3. 2 Optimization Algorithm
3. 3. 3 Results and analysis
3. 4 Conclusion
References
Chapter 4 Statistical Modeling of Multi—Channel SAR Images
4. 1 Introduction
4. 2 Normalized Interferogram
4.3 The Joint Distribution
4. 3. 1 The Known Joint Distribution for Heterogeneous Regions
4. 3.2 The New Joint Distribution for extremely Heterogeneous Regions
4. 3.3 Relationship between distributions
4. 3.4 Parameter estimations
4. 3.5 Experimental analysis
4. 3.6 Conclusion
4.4 The Proposed Distribution for Interferogram's Magnitude of Homogenous Clutter
4.4.1 The T Distribution for Homogeneous Clutter
4.4.2 Parameter Estimators of
……
Chapter 5 Target Detwction Multi—Channel SAR Images
Chapter 6 Statistical Modeling and Target Detwction of PolSAR Images
1.1 Introduction
1.2 Model Classification and Research Contents
1.2.1 Parameter estimation
1.2.2 Goodness—of—fit tests
1.3 Statistical models
1.3.1 Nonparametric models
1.3.2 Parametric models
1.4 Classification of parametric models
1.4.1 The statistical models developed from the product model
1.4.2 The statistical models developed from the generalized central limit theorem
1.4.3 The empirical distributions
1.4.4 Other models
1.5 The Relationship Among The Major Models and Their Applications
1.5.1 The relationship among the parametric statistical models
1.5.2 Summary of the applications of the major models
1.6 Discussion of Future Work
1.7 Conclusions References
Chapter 2 Statistical Modeling of Single—Channel SAIl IInages
2.1 Modeling SAR Images Based on A Generalized Gamma Distribution for Texture Component
2.1.1 The Proposed GFFodel
2.1.2 Parameter Estimator of the GFF Model Based on MoLC
2.1.3 ExperimentaI Results
2.1.4 Appendix 2—A.The Derivation of m—th order moments of the Distribution
2.1.5 Appendix 2—B.Proof of the relationship between Distributions
2.2 An Empirical Distribution for Characterizing the Statistical Properties of SAR Clutter
2.2.1 The Proposed Distribution
2.2.2 The Parameter Estimators of The Proposed Distribution
2.2.3 Experimental Results
2. 2. 5 Appendix 2—C. The Derivation of m—th Order Moments of the Distribution
2. 2.6 Appendix 2—D. The Derivation of the Cumulative Distribution Function of HG
2. 3 An Improved Scheme for Parameter Estimation of G0 Distribution Model in High—Resolution SAR Images
2. 3.1 The G0 Model
2. 3. 2 MoM Based Parameter Estimation
2. 3. 3 MT Based Parameter Estimation
2. 3.4 Our Proposed Parameter Estimation
2. 3.5 Results and Discussion
2.4 Conclusions
References
Chapter 3 Target Detection and Terrain Classification of Single—Channel SAR Images
3. 1 A CFAR Detection Algorithm for Generalized Gamma Distributed Background in High—Resolution SAR Images
3. 1.1 Generalized Gamma Distribution and Its Estimation
3.1.2 CFAR Algorithm Using GFD for Background
3. 1.3 Performance Evaluation
3.2 A Parzen Window Kernel Based CFAR Algorithm for Ship Detection in SAR Images
3.2. 1 Statistical modeling of SAR image based on Parzen window kernel
3.2.2 CFAR detection
3. 2. 3 Experimental results
3. 3 A Markovian classification method for urban areas in High—resolution SAR images
3. 3. 1 Markovian Formalism
3. 3. 2 Optimization Algorithm
3. 3. 3 Results and analysis
3. 4 Conclusion
References
Chapter 4 Statistical Modeling of Multi—Channel SAR Images
4. 1 Introduction
4. 2 Normalized Interferogram
4.3 The Joint Distribution
4. 3. 1 The Known Joint Distribution for Heterogeneous Regions
4. 3.2 The New Joint Distribution for extremely Heterogeneous Regions
4. 3.3 Relationship between distributions
4. 3.4 Parameter estimations
4. 3.5 Experimental analysis
4. 3.6 Conclusion
4.4 The Proposed Distribution for Interferogram's Magnitude of Homogenous Clutter
4.4.1 The T Distribution for Homogeneous Clutter
4.4.2 Parameter Estimators of
……
Chapter 5 Target Detwction Multi—Channel SAR Images
Chapter 6 Statistical Modeling and Target Detwction of PolSAR Images
