基于FCNN和ICAE的SAR图像目标识别方法

喻玲娟; 王亚东; 谢晓春; 林赟; 洪文

doi:10.12000/JR18066

基于FCNN和ICAE的SAR图像目标识别方法

DOI: 10.12000/JR18066

喻玲娟^1,2, ,,
王亚东^1,,
谢晓春^3,,
林赟^2,,
洪文^2,

①.
江西理工大学信息工程学院赣州 341000
②.
中国科学院电子学研究所北京 100190
③.
赣南师范大学物理与电子信息学院赣州 341000

基金项目: 国家自然科学基金（61431018，61501210，61571421），江西省自然科学基金（20161BAB202054），江西省教育厅科技项目（GJJ150684，GJJ170825）

详细信息

作者简介:
喻玲娟(1982–)，女，籍贯江西，博士，江西理工大学副教授，硕士生导师，中国科学院电子学研究所博士后，研究方向为合成孔径雷达信号处理。E-mail: lingjuanyusmile@163.com

王亚东(1993–)，男，籍贯江苏，江西理工大学在读硕士研究生，研究方向为合成孔径雷达自动目标识别。E-mail: wangyadong183@163.com

谢晓春(1975–)，男，籍贯江西，博士，赣南师范大学副教授，硕士生导师，研究方向为合成孔径雷达信号处理。E-mail: xiexiaochun@gnnu.cn

林　赟(1983–)，女，籍贯浙江，博士，中国科学院电子学研究所副研究员，硕士生导师，研究方向为合成孔径雷达3维成像技术、多角度SAR成像基础理论与方法研究。E-mail: ylin@mail.ie.ac.cn

洪　文(1968–)，女，籍贯陕西，博士，中国科学院电子学研究所研究员，博士生导师，主要研究方向为合成孔径雷达成像与系统及其应用、极化/干涉合成孔径雷达数据处理及应用、3维微波成像新概念新体制新方法等。E-mail: whong@mail.ie.ac.cn

通讯作者:
喻玲娟　 lingjuanyusmile@163.com

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出版历程
- 收稿日期: 2018-08-31
- 修回日期: 2018-10-20
- 网络出版日期: 2018-10-28

SAR ATR Based on FCNN and ICAE

Yu Lingjuan^{1,2
, ,},
Wang Yadong^1
,,
Xie Xiaochun^3
,,
Lin Yun^2
,,
Hong Wen^2
,

①.
School of Information Engineering, Jiangxi University of Science and Technology, Ganzhou 341000, China
②.
Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
③.
School of Physics and Electronic Information, Gannan Normal University, Ganzhou 341000, China

Funds: The National Natural Science Foundation of China (61431018, 61501210, 61571421), The Natural Science Foundation of Jiangxi Province (20161BAB202054), The Science and Technology Project of Jiangxi Provincial Education Department (GJJ150684, GJJ170825)

摘要

摘要: 近年来，基于卷积神经网络(Convolutional Neural Network, CNN)的合成孔径雷达(Synthetic Aperture Radar, SAR)图像目标识别得到深入研究。全卷积神经网络(Fully Convolutional Neural Network, FCNN)是CNN结构上的改进，它比CNN能获得更高的识别率，但在训练过程中仍需要大量的带标签训练样本。该文提出一种基于FCNN和改进的卷积自编码器(Improved Convolutional Auto-Encoder, ICAE)的SAR图像目标识别方法，即先用ICAE无监督训练方式获得的编码器网络参数初始化FCNN的部分参数，后用带标签训练样本对FCNN进行训练。基于MSTAR数据集的十类目标分类实验结果表明，在不扩充带标签训练样本的情况下，该方法不仅能获得98.14%的平均正确识别率，而且具有较强的抗噪声能力。
- 合成孔径雷达 /
- 自动目标识别 /
- 全卷积神经网络 /
- 卷积自编码器 /
- 改进的卷积自编码器
Abstract: In recent years, Synthetic Aperture Radar (SAR) image target recognition based on the Convolutional Neural Network (CNN) has attracted a significant amount of attention. Fully CNN (FCNN) is a structural improvement of the CNN, which features a higher recognition rate than CNN, but it still requires a large number of labeled data in the training process. This study aims to propose a method of SAR image target recognition based on FCNN and Improved Convolutional Auto-Encoder (ICAE), where several parameters of FCNN are initialized by the parameters of the ICAE encoder. These parameters are obtained in the unsupervised training mode. Then, the FCNN is trained by the labeled training samples. The experimental results on 10 kinds of target classification based on the MSTAR datasets show that this method cannot only achieve an average of 98.14% correct recognition rate but also feature a strong anti-noise capability when the labeled training samples are unexpanded.
- Synthetic Aperture Radar (SAR) /
- Automatic target recognition /
- Fully Convolutional Neural Network (FCNN) /
- Convolutional Auto-Encoder (CAE) /
- Improved Convolutional Auto-Encoder (ICAE)

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图 1 CAE结构示意图

Figure 1. The structure of CAE

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图 3 FCNN的结构和前向传播

Figure 3. The structure and forward propagation of FCNN

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图 2 基于FCNN和ICAE的识别方法

Figure 2. The recognition method based on FCNN and ICAE

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图 4 ICAE的结构和前向传播

Figure 4. The structure and forward propagation of ICAE

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图 5 10类地面军事目标光学图像及其SAR图像

Figure 5. Ten types of ground military targets: Optical images versus SAR images

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图 6 4种方法的平均正确识别率随带标签的训练样本扩充倍数的变化

Figure 6. The average correct recognition rate of the four methods varies with the multiples of labeled training samples

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图 7 加不同比例噪声的SAR图像

Figure 7. SAR images with noise of different proportions

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图 8 4种方法的平均正确识别率随噪声所占比例的变化

Figure 8. The average correct recognition rate of the four methods varies with the noise proportion

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图 9 不同信噪比的SAR图像

Figure 9. SAR images with different SNR

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图 10 4种方法的平均正确识别率随信噪比的变化

Figure 10. The average correct recognition rate of the four methods varies with SNR

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表 1 训练样本和测试样本数量

Table 1. The number of training and testing images

型号	训练集		测试集
型号	角度	数量	角度	数量
BMP-2	17°	233	15°	195
BTR-70	17°	233	15°	196
BTR-60	17°	256	15°	195
BRDM-2	17°	298	15°	274
T-72	17°	232	15°	196
T-62	17°	299	15°	273
2S1	17°	299	15°	274
D7	17°	299	15°	274
ZIL-131	17°	299	15°	274
ZSU-234	17°	299	15°	274

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表 2 FCNN, ICAE, CNN和CAE的网络结构

Table 2. The network structures of FCNN, ICAE, CNN and CAE

FCNN	ICAE	CNN	CAE
Conv.16@3×3 Conv.16@3×3/stride=2	Conv.16@3×3 Conv.16@3×3/stride=2	Conv.16@3×3 Maxpooling@2×2	Conv.16@3×3 Maxpooling@2×2
Conv.32@3×3 Conv.32@3×3/stride=2	Conv.32@3×3 Conv.32@3×3/stride=2	Conv.32@3×3 Maxpooling@2×2	Conv.32@3×3 Maxpooling@2×2
Conv.64@3×3 Conv.64@3×3/stride=2	Conv.64@3×3 Conv.64@3×3/stride=2	Conv.64@3×3 Maxpooling@2×2	Conv.64@3×3 Maxpooling@2×2
Conv.128@3×3 Conv.128@3×3/stride=2	Conv.128@3×3 Conv.128@3×3/stride=2	Conv.128@3×3 Maxpooling@2×2	Conv.128@3×3 Maxpooling@2×2
Conv.10@4×4 Softmax	Unpooling@2×2 Deconv.64@3×3	Conv.10@4×4 Softmax	Unpooling@2×2 Deconv.64@3×3
	Unpooling@2×2 Deconv.32@3×3		Unpooling@2×2 Deconv.32@3×3
	Unpooling@2×2 Deconv.16@3×3		Unpooling@2×2 Deconv.16@3×3
	Deconv.1@3×3		Deconv.1@3×3

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表 3 基于FCNN和ICAE的识别结果

Table 3. The recognition results based on FCNN and ICAE

目标型号	识别结果										正确识别率 (%)
目标型号	2S1	BMP-2	BRDM-2	BTR-60	BTR-70	D7	T-62	T-72	ZIL-131	ZSU-234	正确识别率 (%)
2S1	265	0	1	0	2	0	0	6	0	0	96.72
BMP-2	1	191	0	0	1	1	0	1	0	0	98.45
BRDM-2	0	0	270	3	0	0	0	0	1	0	98.54
BTR-60	0	0	2	186	0	0	1	0	2	4	95.38
BTR-70	3	0	0	0	193	0	0	0	0	0	98.47
D7	0	0	2	0	0	272	0	0	0	0	99.27
T-62	0	0	0	0	0	0	270	0	0	3	98.90
T-72	0	1	0	0	1	0	0	194	0	0	98.98
ZIL-131	0	0	0	0	0	1	2	0	270	1	98.54
ZSU-234	0	0	0	0	0	3	1	1	0	269	98.18
平均正确识别率(%)	98.14

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表 4 基于不同方法的实验结果对比

Table 4. The comparison of experimental results based on different methods

目标型号	识别率(%)
目标型号	FCNN+ICAE	CNN+CAE	FCNN	CNN
2S1	96.72	95.99	94.16	96.72
BMP-2	97.95	97.44	96.41	94.36
BRDM-2	98.54	95.99	96.35	95.26
BTR-60	95.38	91.79	95.38	96.41
BTR-70	98.47	99.49	98.98	93.88
D7	99.27	96.35	98.54	97.81
T-62	98.90	97.07	90.84	93.41
T-72	98.98	98.98	98.47	97.96
ZIL-131	98.54	98.91	97.81	96.35
ZSU-234	98.18	98.91	99.64	94.89
平均正确识别率(%)	98.14	97.11	96.58	95.71

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