合成孔径雷达图像目标识别
正版图书，可开发票，请放心购买。
￥ 62.01 6.3折 ￥ 98 全新
库存34件
广东广州
认证卖家担保交易快速发货售后保障
作者刘明，陈士超著
出版社电子工业出版社
ISBN9787121476297
出版时间2024-04
装帧平装
开本其他
定价98元
货号15839228
上书时间2024-06-27
- 店主推荐
- 最新上架
商品详情
- 品相描述：全新
- 商品描述
-
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McMaster University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余项重量和省部级科研项目。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····························································.98 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··································133 10.2.1 第一阶段（训练阶段）的结构保持································.133 10.2.2 第二阶段（测试阶段）的结构保持································.135 10.3 试验结果与分析····································································140 10.3.1 目标的类别识别·························································.141 10.3.2 目标的型号识别·························································.145 10.4 本章小结·············································································150 第11 章 总结与展望···········································································151 11.1 全书总结·············································································151 11.2 工作展望·············································································153 参考文献···························································································155
内容摘要
本书以作者在合成孔径雷达图像目标识别领域十多年的研究为主体，阐明了合成孔径雷达图像的统计特性、稀疏特征及流形特征，着重介绍了基于局部保持特性和稀疏表示的合成孔径雷达图像目标识别研究成果。本书内容共11章，包括绪论、相关理论、目标识别技术，以及总结与展望等方面，适合高等院校相关专业研究生，以及合成孔径雷达目标识别领域的研究人员和工程技术人员阅读参考。
主编推荐
雷达技术 合成孔径雷达 图像处理 图像识别 SAR图像
精彩内容
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。
— 没有更多了 —
以下为对购买帮助不大的评价