基于半监督学习的SAR目标检测网络

杜兰; 魏迪; 李璐; 郭昱辰

doi:10.11999/JEIT190783

基于半监督学习的SAR目标检测网络

doi: 10.11999/JEIT190783

西安电子科技大学雷达信号处理国家重点实验室西安 710071

基金项目: 国家自然科学基金(61771362, U1833203, 61671354)，高等学校学科创新引智计划(B18039)，陕西省重点科技创新团队计划

详细信息

作者简介:
杜兰：女，1980年生，教授，博士生导师，研究方向为统计信号处理、雷达信号处理、机器学习及其在雷达目标检测与识别方面的应用

魏迪：男，1995年生，硕士生，研究方向为雷达目标检测与识别，机器学习等

李璐：女，1992年生，博士生，研究方向为机器学习和目标检测识别等

郭昱辰：男，1992年生，博士生，研究方向为雷达目标检测与识别

通讯作者:
杜兰　dulan@mail.xidian.edu.cn

中图分类号: TN957.51
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出版历程
- 收稿日期: 2019-10-12
- 修回日期: 2019-12-05
- 网络出版日期: 2019-12-09
- 刊出日期: 2020-01-21

SAR Target Detection Network via Semi-supervised Learning

National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds: The National Science Foundation of China (61771362, U1833203, 61671354), 111 Project (B18039), Shaanxi Provience Innovation Team Project

摘要

摘要:
现有的基于卷积神经网络(CNN)的合成孔径雷达(SAR)图像目标检测算法依赖于大量切片级标记的样本，然而对SAR图像进行切片级标记需要耗费大量的人力和物力。相对于切片级标记，仅标记图像中是否含有目标的图像级标记较为容易。该文利用少量切片级标记的样本和大量图像级标记的样本，提出一种基于卷积神经网络的半监督SAR图像目标检测方法。该方法的目标检测网络由候选区域提取网络和检测网络组成。半监督训练过程中，首先使用切片级标记的样本训练目标检测网络，训练收敛后输出的候选切片构成候选区域集；然后将图像级标记的杂波样本输入网络，将输出的负切片加入候选区域集；接着将图像级标记的目标样本也输入网络，对输出结果中的正负切片进行挑选并加入候选区域集；最后使用更新后的候选区域集训练检测网络。更新候选区域集和训练检测网络交替迭代直至收敛。基于实测数据的实验结果证明，所提方法的性能与使用全部样本进行切片级标记的全监督方法的性能相差不大。
- 合成孔径雷达 /
- 目标检测 /
- 半监督学习 /
- 卷积神经网络
Abstract:
The current Synthetic Aperture Radar (SAR) target detection methods based on Convolutional Neural Network (CNN) rely on a large amount of slice-level labeled train samples. However, it takes a lot of labor and material resources to label the SAR images at slice-level. Compared to label samples at slice-level, it is easier to label them at image-level. The image-level label indicates whether the image contains the target of interest or not. In this paper, a semi-supervised SAR image target detection method based on CNN is proposed by using a small number of slice-level labeled samples and a large number of image-level labeled samples. The target detection network of this method consists of region proposal network and detection network. Firstly, the target detection network is trained using the slice-level labeled samples. After training convergence, the output slices constitute the candidate region set. Then, the image-level labeled clutter samples are input into the network and then the negative slices of the output are added to the candidate region set. Next, the image-level labeled target samples are input into the network as well. After selecting the positive and negative slices in the output of the network, they are added to the candidate region set. Finally, the detection network is trained using the updated candidate region set. The processes of updating candidate region set and training detection network alternate until convergence. The experimental results based on the measured data demonstrate that the performance of the proposed method is similar to the fully supervised training method using a much larger set of slice-level samples.
- SAR /
- Target detection /
- Semi-supervised learning /
- Convolutional Neural Network (CNN)

HTML全文

图 1 半监督SAR图像目标检测方法

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图 2 特征提取网络

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图 3 数据集示例

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图 4 MiniSAR数据集：Gaussian-CFAR的检测结果

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图 5 MiniSAR数据集：Faster R-CNN-少部分切片级标记的检测结果

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图 6 MiniSAR数据集：Faster R-CNN-全部切片级标记的检测结果

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图 7 MiniSAR数据集：文献[14]方法的检测结果

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图 8 MiniSAR数据集：文献[15]方法的检测结果

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图 9 MiniSAR数据集：本文方法的检测结果

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图 10 FARADSAR数据集：Gaussian-CFAR的检测结果

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图 11 FARADSAR数据集：Faster R-CNN-少部分切片级标记的检测结果

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图 12 FARADSAR数据集：Faster R-CNN-全部切片级标记的检测结果

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图 13 FARADSAR数据集：文献[14]方法的检测结果

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图 14 FARADSAR数据集：文献[15]方法的检测结果

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图 15 FARADSAR数据集：本文方法的检测结果

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表 1 不同方案的实验结果

负包数量	挑选的切片	$P$	$R$	F1-score
0	正切片	0.6397	0.7500	0.6905
	负切片	0.8833	0.4569	0.6023
	正切片+负切片	0.7387	0.7069	0.7225
10	正切片	0.6797	0.7500	0.7131
	负切片	0.7917	0.4914	0.6064
	正切片+负切片	0.7573	0.6724	0.7123
20	正切片	0.7658	0.7328	0.7489
	负切片	0.8382	0.4914	0.6196
	正切片+负切片	0.8137	0.7155	0.7615
30	正切片	0.8202	0.6293	0.7122
	负切片	0.8413	0.4569	0.5922
	正切片+负切片	0.8675	0.6207	0.7236
40	正切片	0.8111	0.6293	0.7087
	负切片	0.8667	0.4483	0.5909
	正切片+负切片	0.8352	0.6552	0.7343

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表 2 不同方法的实验结果

不同方法	MiniSAR数据集			FARADSAR数据集
不同方法	$P$	$R$	F1-score	$P$	$R$	F1-score
Gaussian-CFAR	0.3789	0.7966	0.5135	0.2813	0.4671	0.3512
Faster R-CNN-少部分切片级标记	0.6455	0.6121	0.6283	0.7370	0.8813	0.8027
Faster R-CNN-全部切片级标记	0.8073	0.7586	0.7822	0.7760	0.9479	0.8534
文献[14]方法	0.5814	0.9806	0.7285	0.4506	0.7325	0.5580
文献[15]方法	0.4699	0.7480	0.5772	0.3744	0.7945	0.5090
本文方法	0.8137	0.7155	0.7615	0.8035	0.8813	0.8406

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