合成孔径雷达图像目标识别
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作者刘明//陈士超|
出版社电子工业
ISBN9787121476297
出版时间2024-04
装帧其他
开本其他
定价98元
货号32054916
上书时间2024-07-24
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作者简介
刘明，工学博士，副教授，硕士生导师。2009年获西安电子科技大学信息对抗技术专业工学学士学位，2015年获西安电子科技大学模式识别与智能系统专业工学博士学位。2019年-2020年为加拿大McMasterUniversity访学学者。主要研究方向为：目标检测与目标识别。入选陕西省科协青年人才托举计划，获国际无线电科学联盟（URSI）\"青年科学家”奖，获陕西省计算机学会\"计算机领域优秀青年专家”称号。主持和参与了包括国家自然科学基金、国家重大基础研究计划、装备预先研究、陕西省自然科学基金等10余项国家级和省部级科研项目。发表学术论文60余篇，授权国家发明专利10项（部分已转化）。
目录
第1 章 绪论························································································1
1.1 研究背景及研究意义··································································1
1.2 国内外研究现状········································································3
1.3 本书内容介绍········································································· 10
第2 章 基于局部保持特性和混合高斯分布的SAR 图像目标识别··················· 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 图像目标识别···································· 16
2.3.1 基于混合高斯分布的似然函数建模····································· 17
2.3.2 基于局部保持特性的先验函数建模····································· 17
2.3.3 参数估计······································································ 18
2.4 试验结果与分析······································································ 22
2.5 本章小结··············································································· 26
第3 章 基于局部保持特性和Gamma 分布的SAR 图像目标识别··················· 27
3.1 算法概述··············································································· 27
3.2 SAR 图像的乘性相干斑模型······················································· 28
3.3 基于LPP-Gamma 算法的SAR 图像目标识别·································· 29
3.3.1 基于Gamma 分布构建似然函数········································· 29
3.3.2 基于局部保持特性构建先验函数········································ 30
3.3.3 参数估计······································································ 33
3.4 试验结果与分析······································································ 37
3.4.1 SAR 图像目标识别结果··················································· 37
3.4.2 修正相似度矩阵的有效性验证··········································· 39
3.5 本章小结··············································································· 41
第4 章 基于结构保持投影的SAR 图像目标识别········································ 42
4.1 算法概述··············································································· 42
4.2 基于CDSPP 算法的SAR 图像目标识别········································ 43
4.2.1 CDSPP 算法·································································· 43
4.2.2 差异度矩阵分析····························································· 45
4.3 试验结果与分析······································································ 49
4.3.1 目标的类别识别····························································· 51
4.3.2 目标的型号识别····························································· 53
4.3.3 构建差异度矩阵的优势···················································· 57
4.4 本章小结··············································································· 59
第5 章 基于类别稀疏表示的SAR 图像目标识别········································ 60
5.1 算法概述··············································································· 60
5.2 SAR 图像的稀疏表示模型·························································· 61
5.3 SAR 图像的类别稀疏表示模型···················································· 62
5.3.1 方位角敏感特性····························································· 62
5.3.2 测试样本建模································································ 64
5.3.3 稀疏向量求解································································ 66
5.4 基于LSR 算法的SAR 图像目标识别············································ 67
5.5 试验结果与分析······································································ 70
5.5.1 目标的类别识别····························································· 70
5.5.2 目标的型号识别····························································· 72
5.6 本章小结··············································································· 76
第6 章 基于乘性稀疏表示和Gamma 分布的SAR 图像目标识别··················· 77
6.1 算法概述··············································································· 77
6.2 乘性稀疏表示算法··································································· 78
6.3 试验结果与分析······································································ 80
6.3.1 目标的类别识别····························································· 81
6.3.2 目标的型号识别····························································· 82
6.4 本章小结··············································································· 88
第7 章 基于判别统计字典学习的SAR 图像目标识别·································· 89
7.1 算法概述··············································································· 89
7.2 基于判别统计字典学习（DSDL）的SAR 图像目标识别··················· 90
7.2.1 统计字典学习（SDL）算法·············································· 90
7.2.2 融入判别因子字典·························································· 93
7.2.3 算法的计算复杂度分析···················································· 94
7.3 试验结果与分析······································································ 96
7.3.1 目标的类别识别····························································· 97
7.3.2 目标的型号识别····························································· 8
7.4 本章小结··············································································103
第8 章 基于Dempster-Shafer 证据理论融合多稀疏表示和样本统计特性的SAR
图像目标识别·········································································105
8.1 算法概述··············································································105
8.2 Dempster-Shafer 证据理论·························································106
8.3 基于Dempster-Shafer 证据理论的融合算法···································107
8.3.1 SAR 图像的多稀疏表示················································· 107
8.3.2 基本概率分配函数的推导··············································· 113
8.4 试验结果与分析·····································································117
8.5 本章小结··············································································119
第9 章 基于Dempster-Shafer 证据理论和稀疏表示的SAR 图像目标识别······120
9.1 算法概述··············································································120
9.2 基于Dempster-Shafer 证据理论的融合算法···································121
9.2.1 构建基于稀疏表示的基本概率分配函数····························· 121
9.2.2 融合算法···································································· 123
9.3 试验结果与分析·····································································125
9.3.1 目标的类别识别··························································· 126
9.3.2 目标的型号识别··························································· 128
9.4 本章小结··············································································131
第10 章 基于两阶段稀疏结构表示的SAR 图像目标识别····························132
10.1 算法概述·············································································132
10.2 基于两阶段稀疏结构表示（TSSR）的算法·····
内容摘要
本书共计11章，第1章对合成孔径雷达（SAR）目标识别进行了概述；第2章介绍了基于局部保持特性和混合高斯分布的SAR目标识别；第3章介绍了基于局部保持特性和Gamma分布的SAR目标识别；第4章介绍了基于结构保持投影的SAR目标识别；第5章介绍了基于类别稀疏表示的SAR目标识别；第6章介绍了基于乘性稀疏表示和Gamma分布的SAR目标识别；第7章介绍了基于判别统计字典学习的SAR目标识别；第8章介绍了于Dempster-Shafer证据理论融合多稀疏描述和样本统计特性的SAR目标识别；第9章介绍了基于Dempster-Shafer证据理论和稀疏表示的SAR目标识别；第10章介绍了基于两阶段稀疏结构表示的SAR目标识别；第11章探讨了未来合成孔径雷达目标识别可能的发展方向。
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