第1 章 緒論························································································1
1.1 研究背景及研究意義··································································1
1.2 國(guó)內(nèi)外研究現(xiàn)狀········································································3
1.3 本書內(nèi)容介紹········································································.10
第2 章 基于局部保持特性和混合高斯分布的SAR 圖像目標(biāo)識(shí)別··················.14
2.1 算法概述··············································································.14
2.2 局部保持投影算法··································································.15
2.3 基于LPP-GMD 算法的SAR 圖像目標(biāo)識(shí)別···································.16
2.3.1 基于混合高斯分布的似然函數(shù)建�！ぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁ�.17
2.3.2 基于局部保持特性的先驗(yàn)函數(shù)建模····································.17
2.3.3 參數(shù)估計(jì)·····································································.18
2.4 試驗(yàn)結(jié)果與分析·····································································.22
2.5 本章小結(jié)··············································································.26
第3 章 基于局部保持特性和Gamma 分布的SAR 圖像目標(biāo)識(shí)別··················.27
3.1 算法概述··············································································.27
3.2 SAR 圖像的乘性相干斑模型······················································.28
3.3 基于LPP-Gamma 算法的SAR 圖像目標(biāo)識(shí)別·································.29
3.3.1 基于Gamma 分布構(gòu)建似然函數(shù)········································.29
3.3.2 基于局部保持特性構(gòu)建先驗(yàn)函數(shù)·······································.30
3.3.3 參數(shù)估計(jì)·····································································.33
3.4 試驗(yàn)結(jié)果與分析·····································································.37
3.4.1 SAR 圖像目標(biāo)識(shí)別結(jié)果··················································.37
3.4.2 修正相似度矩陣的有效性驗(yàn)證··········································.39
3.5 本章小結(jié)··············································································.41
第4 章 基于結(jié)構(gòu)保持投影的SAR 圖像目標(biāo)識(shí)別·······································.42
4.1 算法概述··············································································.42
4.2 基于CDSPP 算法的SAR 圖像目標(biāo)識(shí)別·······································.43
4.2.1 CDSPP 算法·································································.43
4.2.2 差異度矩陣分析····························································.45
4.3 試驗(yàn)結(jié)果與分析·····································································.49
4.3.1 目標(biāo)的類別識(shí)別····························································.51
4.3.2 目標(biāo)的型號(hào)識(shí)別····························································.53
4.3.3 構(gòu)建差異度矩陣的優(yōu)勢(shì)···················································.57
4.4 本章小結(jié)··············································································.59
第5 章 基于類別稀疏表示的SAR 圖像目標(biāo)識(shí)別·······································.60
5.1 算法概述··············································································.60
5.2 SAR 圖像的稀疏表示模型·························································.61
5.3 SAR 圖像的類別稀疏表示模型···················································.62
5.3.1 方位角敏感特性····························································.62
5.3.2 測(cè)試樣本建�！ぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁぁ�.64
5.3.3 稀疏向量求解·······························································.66
5.4 基于LSR 算法的SAR 圖像目標(biāo)識(shí)別···········································.67
5.5 試驗(yàn)結(jié)果與分析·····································································.70
5.5.1 目標(biāo)的類別識(shí)別····························································.70
5.5.2 目標(biāo)的型號(hào)識(shí)別····························································.72
5.6 本章小結(jié)··············································································.76
第6 章 基于乘性稀疏表示和Gamma 分布的SAR 圖像目標(biāo)識(shí)別··················.77
6.1 算法概述··············································································.77
6.2 乘性稀疏表示算法··································································.78
6.3 試驗(yàn)結(jié)果與分析·····································································.80
6.3.1 目標(biāo)的類別識(shí)別····························································.81
6.3.2 目標(biāo)的型號(hào)識(shí)別····························································.82
6.4 本章小結(jié)··············································································.88
第7 章 基于判別統(tǒng)計(jì)字典學(xué)習(xí)的SAR 圖像目標(biāo)識(shí)別·································.89
7.1 算法概述··············································································.89
7.2 基于判別統(tǒng)計(jì)字典學(xué)習(xí)（DSDL）的SAR 圖像目標(biāo)識(shí)別··················.90
7.2.1 統(tǒng)計(jì)字典學(xué)習(xí)（SDL）算法·············································.90
7.2.2 融入判別因子字典·························································.93
7.2.3 算法的計(jì)算復(fù)雜度分析···················································.94
7.3 試驗(yàn)結(jié)果與分析·····································································.96
7.3.1 目標(biāo)的類別識(shí)別····························································.97
7.3.2 目標(biāo)的型號(hào)識(shí)別····························································.98
7.4 本章小結(jié)··············································································103
第8 章 基于Dempster-Shafer 證據(jù)理論融合多稀疏表示和樣本統(tǒng)計(jì)特性的SAR
圖像目標(biāo)識(shí)別·········································································105
8.1 算法概述··············································································105
8.2 Dempster-Shafer 證據(jù)理論·························································106
8.3 基于Dempster-Shafer 證據(jù)理論的融合算法···································107
8.3.1 SAR 圖像的多稀疏表示················································.107
8.3.2 基本概率分配函數(shù)的推導(dǎo)··············································.113
8.4 試驗(yàn)結(jié)果與分析·····································································117
8.5 本章小結(jié)··············································································119
第9 章 基于Dempster-Shafer 證據(jù)理論和稀疏表示的SAR 圖像目標(biāo)識(shí)別······120
9.1 算法概述··············································································120
9.2 基于Dempster-Shafer 證據(jù)理論的融合算法···································121
9.2.1 構(gòu)建基于稀疏表示的基本概率分配函數(shù)····························.121
9.2.2 融合算法···································································.123
9.3 試驗(yàn)結(jié)果與分析·····································································125
9.3.1 目標(biāo)的類別識(shí)別··························································.126
9.3.2 目標(biāo)的型號(hào)識(shí)別··························································.128
9.4 本章小結(jié)··············································································131
第10 章 基于兩階段稀疏結(jié)構(gòu)表示的SAR 圖像目標(biāo)識(shí)別····························132
10.1 算法概述·············································································132
10.2 基于兩階段稀疏結(jié)構(gòu)表示（TSSR）的算法··································133
10.2.1 第一階段（訓(xùn)練階段）的結(jié)構(gòu)保持································.133
10.2.2 第二階段（測(cè)試階段）的結(jié)構(gòu)保持································.135
10.3 試驗(yàn)結(jié)果與分析····································································140
10.3.1 目標(biāo)的類別識(shí)別·························································.141
10.3.2 目標(biāo)的型號(hào)識(shí)別·························································.145
10.4 本章小結(jié)·············································································150
第11 章 總結(jié)與展望···········································································151
11.1 全書總結(jié)·············································································151
11.2 工作展望·············································································153
參考文獻(xiàn)···························································································155