基于无训练单样本有效特征的SAR目标检测

曾丽娜; 周德云; 李枭扬; 张堃

doi:10.12000/JR16114

基于无训练单样本有效特征的SAR目标检测

DOI: 10.12000/JR16114

西北工业大学电子信息学院西安 710072

基金项目:

国家自然科学基金 61401363

详细信息

作者简介:
曾丽娜 (1982–), 河北昌黎人, 女, 西北工业大学博士研究生, 研究方向为SAR图像特征分析、SAR目标检测、SAR图像配准。E-mail:zenglina@mail.nwpu.edu.cn

周德云 (1964–), 浙江义乌人, 男, 教授, 博士生导师, 西北工业大学电子信息学院院长, 中国航空学会航空武器系统专业分会委员兼秘书长, 研究方向为航空火力与指挥控制、先进综合控制理论及应用。E-mail:dyzhou@nwpu.edu.cn

李枭扬 (1989–), 陕西西安人, 男, 西北工业大学博士研究生, 研究方向为指挥控制、SAR图像检测。E-mail:winnerlxylee@126.com

张堃 (1982–), 陕西西安人, 男, 西北工业大学博士, 副教授, 中国航空学会航空武器系统分会干事, 研究方向为威胁评估、任务规划、图像处理。E-mail:kunzhang@nwpu.edu.cn

通讯作者:
曾丽娜, E-mail:zenglina@mail.nwpu.edu.cn

中图分类号: TN959
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- 被引次数: 0
出版历程
- 收稿日期: 2016-10-12
- 修回日期: 2016-11-29
- 网络出版日期: 2017-04-28

Novel SAR Target Detection Algorithm Using Free Training

School of Electronic Information, Northwestern Polytechnical University, Xi'an 710072, China

Funds:

The National Natural Science Foundation of China 61401363

摘要

摘要: 该文提出了一种基于单样本特征提取的SAR感兴趣目标检测方法，充分利用选取的单个目标样本的有效特征实现SAR图像中同类目标的检测。该方法首先检测SAR图像中的潜在感兴趣目标，然后分别提取目标样本和潜在目标中的面积特征和纹理描述特征，并通过不同的匹配方式逐步剔除潜在兴趣目标中的虚假目标。文中提出的纹理描述通过提取具有一定旋转不变性的区域特征描述的方式来适应目标的不同方位，并对SAR噪声和形变具有一定的抑制作用。与多种特征描述方式的对比测试表明了该文提出方法的可行性和有效性。
- 合成孔径雷达 /
- 特征提取 /
- 目标检测 /
- 快速鲁棒性特征算法
Abstract: A detection method for Synthetic Aperture Radar (SAR) targets based on single sample feature extraction is proposed. Similar targets in a SAR image are detected according to the effective features of the selected single target sample. First, the potential targets of interest in a SAR image are detected, and the area features and texture features are extracted from the target sample and potential targets, respectively. Then, the false targets are eliminated from the potential targets via different matching methods. The proposed method for texture description in this paper can be adopted for targets with different attitudes by extracting the rotationinvariance features of the local region; these features can deal with speckle noise and deformation. The experimental results show the feasibility and validity of the proposed method.
- Synthetic Aperture Radar (SAR) /
- Feature extraction /
- Object detection /
- Speeded up robust features

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图 1 SAR目标检测系统原理图

Figure 1. The theory of the SAR target detection system

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图 2 SURF特征点描述

Figure 2. The SURF descriptor of each keypoint

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图 3 PSURF描述符建立

Figure 3. The construction of PSURF descriptor

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图 4 图像坐标变换

Figure 4. The transformation of coordinates of the SAR image

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图 5 图像的Haar响应

Figure 5. The Haar respondence of the image

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图 6 目标检测过程

Figure 6. The process of target detection

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图 7 本文方法与CFAR检测对比结果

Figure 7. The experiment results comparing the proposed method with CFAR detector

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图 8 几种对比特征的检测结果

Figure 8. The detection results by different feature descriptors

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图 9 包含装甲车的SAR图像

Figure 9. The SAR image with armored cars

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图 10 不同特征描述符的装甲车检测结果

Figure 10. The armored car detection results by different descriptors

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图 11 包含飞机目标的双基SAR图像

Figure 11. The bistatic SAR image with airplanes

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图 12 不同特征描述符的飞机检测结果

Figure 12. The airplane detection results by different descriptors

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表 1 不同特征提取方法的检测结果

Table 1. The detection results by different feature extraction methods

特征提取	虚警率	检测率	目标与非目标分离度
SURF+upright	0.22	0.54	■○○○○○○■○○○○○○■■■○■
SURF	0.22	0.54	■○○○○○○■○○○○○○■■■○■
SIFT	0.22	0.54	■○○○○○■○○○○○○○■■■○■
DAISY	0.20	0.62	■○○○○○○■○○○○○○■○■■■
PSURF	0	0.92	○○○○○○○○○○○○■■■■○■■
注：○表示目标；■表示非目标

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表 2 不同特征提取方法的装甲车检测结果

Table 2. The armored car detection results by different feature extraction methods

特征提取	虚警率	检测率
SURF	0.27	0.76
SIFT	0.26	0.80
DAISY	0.27	0.76
PSURF	0.23	0.88

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表 3 不同特征提取方法的飞机检测结果

Table 3. The airplane detection results by different feature extraction methods

特征提取	虚警率	检测率
SURF	0	0.750
SIFT	0	0.875
DAISY	0	0.875
PSURF	0	1.000

下载: 导出CSV

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