基于监督非相干字典学习的极化SAR图像舰船目标检测

文伟; 王英华; 冯博; 刘宏伟

doi:10.16383/j.aas.2015.c140530

基于监督非相干字典学习的极化SAR图像舰船目标检测

doi: 10.16383/j.aas.2015.c140530

文伟^1,2,
王英华^1,2, ,,
冯博^1,2,
刘宏伟^1,2

1.
西安电子科技大学雷达信号处理国家重点实验室西安 710071;
2.
西安电子科技大学信息感知技术协同创新中心西安 710071

基金项目:

国家自然科学基金(61201292,61322103,61372132),全国优秀博士学位论文作者专项资金资助项目(FANEDD-201156),中央高校基本科研业务费专项资金资助

详细信息

作者简介:
文伟西安电子科技大学雷达信号处理国家重点实验室博士研究生.2010年获得西安电子科技大学机电工程学院学士学位.主要研究方向为雷达目标检测.E-mail:wenwei8114@163.com

冯博西安电子科技大学雷达信号处理国家重点实验室博士.2015年获得西安电子科技大学电子工程学院博士学位.主要研究方向为雷达目标识别.E-mail:ivyleague007@126.com

刘宏伟西安电子科技大学电子工程学院教授.主要研究方向为雷达信号处理,MIMO雷达,雷达目标识别,自适应信号处理,认知雷达.E-mail:hwliu@xidian.edu.cn

通讯作者:
王英华西安电子科技大学电子工程学院副教授.2010年获得西安交通大学控制科学与工程专业博士学位.主要研究方向为合成孔径雷达自动目标识别,极化SAR数据解译,SAR图像处理.本文通信作者.E-mail:yhwang@xidian.edu.cn

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- 被引次数: 0
出版历程
- 收稿日期: 2014-07-15
- 修回日期: 2015-07-27
- 刊出日期: 2015-11-20

Supervised Incoherent Dictionary Learning for Ship Detection withPolSAR Images

WEN Wei^1,2,
WANG Ying-Hua^{1,2
, ,},
FENG Bo^1,2,
LIU Hong-Wei^1,2

1.
National Laboratory of Radar Signal Processing, Xidian University, Xi'an 710071;
2.
Collaborative Innovation Center of Information Sensing and Understanding at Xidian University, Xi'an 710071

Funds:

Supported by National Natural Science Foundation of China (61201292, 61322103, 61372132), Program for New Century Excellent Talents in University (FANEDD-201156), and the Fundamental Research Funds for the Central Universities

摘要

摘要: 提出了一种结构化非相干字典学习算法 (Structured incoherent dictionary learning, SIDL),并将该方法应用于极化SAR (Polarimetric synthetic aperture radar, PoLSAR)图像舰船目标检测. 在字典学习阶段,构建了一个新的目标函数,为了降低子字典对交叉样本的稀疏表示能力, 将子字典对交叉样本的重构能量约束及子字典互相干性约束加入到字典学习目标函数中. 通过这两个约束, 降低了子字典对交叉样本的表示能力,目标和杂波的极化特征矢量在学习获得的字典下具有良好的区分特性. 该方法不依赖于目标后向散射能量,只利用学习获得的极化字典,根据测试样本在极化字典下的稀疏表示进行目标的检测. 实验采用RADARSAT-2数据进行了验证,对比实验结果表明,本文提出的方法可以更好地抑制杂波,对弱小目标实现检测,获得了更好的检测效果.
- 极化合成孔径雷达 /
- 监督字典学习 /
- 结构非相干 /
- 能量独立 /
- 舰船检测
Abstract: This paper presents a supervised structured incoherent dictionary learning (SIDL) algorithm and applies it to ship detection using polarimetric synthetic aperture radar (PolSAR) images. In the dictionary learning stage, to weaken the sub-dictionary reconstruction ability to cross-samples, a penalty of cross-representation and a penalty of sub-dictionary cross-incoherence are added to the objection function. So as to restrict the reconstruction of target samples by the clutter sub-dictionary. Then, several statistics are defined with the sparse representation under the learned supervised structured incoherent dictionary. The proposed detection algorithm of the proposed detection algorithm does not depend on the intensity but on the quality of the learned dictionary. The performance is verified using the RADARSAT-2 data set, by comparing it with the partial target detection (PTD) method, the reflection symmetry detection method (RSD), the polarimetric whitening filter (PWF) and the intenstity detector (SPAN). The experimental results show that the proposed method can suppress the clutter better and achieve a better detection performance.
- Polarimetric synthetic aperture radar (PolSAR) /
- supervised dictionary learning /
- structured incoherence /
- ship detection /
- intensity independent

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参考文献(31)

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