基于无监督域适应的仿真辅助SAR目标分类方法及模型可解释性分析

吕小玲; 仇晓兰; 俞文明; 徐丰

doi:10.12000/JR21179

基于无监督域适应的仿真辅助SAR目标分类方法及模型可解释性分析

doi: 10.12000/JR21179

吕小玲^{1, 2, 3},
仇晓兰^{1, 2, ,},
俞文明⁴,
徐丰⁵

1.
中国科学院空天信息创新研究院北京 100094
2.
中国科学院空间信息处理与应用系统技术重点实验室北京 100190
3.
中国科学院大学电子电气与通信工程学院北京 100049
4.
东南大学毫米波国家重点实验室南京 210096
5.
复旦大学电磁波信息科学教育部重点实验室上海 200433

基金项目: 国家自然科学基金(61991421)

详细信息

作者简介:
吕小玲(1997–)，女，四川人，中国科学院空天信息创新研究院在读硕士，研究方向为SAR目标识别

仇晓兰(1982–)，女，江苏人，中国科学院空天信息创新研究院研究员，博士生导师，主要研究方向为SAR成像处理、SAR图像理解，担任IEEE高级会员、IEEE地球科学与遥感快报副主编、雷达学报青年编委

俞文明(1980–)，男，浙江人，东南大学信息科学与工程学院副研究员，主要研究方向为电磁场数值计算

徐　丰(1982–)，男，浙江人，复旦大学信息科学与工程学院教授，主要研究方向为SAR图像解译、电磁散射建模、人工智能，担任IEEE地球科学与遥感快报副主编、IEEE地球科学与遥感学会上海分会主席

通讯作者:
仇晓兰 xlqiu@mail.ie.ac.cn

责任主编：杜兰 Corresponding Editor: DU Lan
中图分类号: TP753
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- 被引次数: 0
出版历程
- 收稿日期: 2021-11-14
- 修回日期: 2022-01-13
- 网络出版日期: 2022-02-16
- 刊出日期: 2022-02-28

Simulation-assisted SAR Target Classification Based on Unsupervised Domain Adaptation and Model Interpretability Analysis

LYU Xiaoling^{1, 2, 3},
QIU Xiaolan^{1, 2
, ,},
YU Wenming⁴,
XU Feng⁵

1.
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
2.
Key Laboratory of Technology in Geospatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China
3.
School of Electronic, Electrical and Communication Engineering, University of Chinese Academy of Sciences, Beijing 100049, China
4.
State Key Laboratory of Millimeter Waves, Southeast University, Nanjing 210096, China
5.
Key Laboratory for Information Science of Electromagnetic Waves, Fudan University, Shanghai 200433, China

Funds: The National Natural Science Foundation of China (61991421)

More Information

Corresponding author: QIU Xiaolan, xlqiu@mail.ie.ac.cn

摘要

摘要: 卷积神经网络(CNN)在光学图像分类领域中得到广泛应用，然而，合成孔径雷达(SAR)图像样本标注难度大、成本高，难以获取满足CNN训练所需的样本数量。随着SAR仿真技术的发展，生成大量带标签的仿真SAR图像并不困难。然而仿真SAR图像样本与真实样本间难免存在差异，往往难以直接支撑实际样本的分类任务。为此，该文提出了一种基于无监督域适应的仿真辅助SAR目标分类方法，集成了多核最大均值差异(MK-MMD)和域对抗训练，以解决由仿真图像分类任务迁移到真实图像分类任务中的域偏移问题。进一步使用逐层相关性传播(LRP)和对比逐层相关性传播(CLRP)两种可解释性方法，对域适应前后的模型进行了解释分析。实验结果表明，该文方法通过修正模型对输入数据的关注区域，找到了域不变的分类特征，显著提升了模型在真实SAR数据上的分类准确率。
- 合成孔径雷达(SAR) /
- 目标分类 /
- 卷积神经网络(CNN) /
- 无监督域适应 /
- 可解释性
Abstract: Convolutional Neural Networks (CNNs) are widely used in optical image classification. In the case of Synthetic Aperture Radar (SAR) images, obtaining sufficient training examples for CNNs is challenging due to the difficulties in and high cost of data annotation. Meanwhile, with the advancement of SAR image simulation technology, generating a large number of simulated SAR images with annotation is not difficult. However, due to the inevitable difference between simulated and real SAR images, it is frequently difficult to directly support the real SAR image classification. As a result, this study proposes a simulation-assisted SAR target classification method based on unsupervised domain adaptation. The proposed method integrates Multi-Kernel Maximum Mean Distance (MK-MMD) with domain adversarial training to address the domain shift problem encountered during task transition from simulated to real-world SAR image classification. Furthermore, Layer-wise Relevance Propagation (LRP) and Contrastive Layer-wise Relevance Propagation (CLRP) are utilized to explore how the proposed method influences the model decision. The experimental results show that by modifying the focus areas of the model to obtain domain-invariant features for classification, the proposed method can significantly improve classification accuracy.
- Synthetic Aperture Radar (SAR) /
- Target classification /
- Convolutional Neural Network (CNN) /
- Unsupervised Domain Adaptation (UDA) /
- Interpretability

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图 1 特征是否具备域不变性对找到不同域通用分类器的影响

Figure 1. The influence of domain invariance of features on finding a general classifier between different domains

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图 2 模型框架示意图

Figure 2. Schematic diagram of the model framework

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图 3 网络结构示意图

Figure 3. Schematic diagram of the model structure

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图 4 LRP原理示意图

Figure 4. An overview of LRP

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图 5 CLRP原理示意图

Figure 5. An overview of CLRP

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图 6 SAR目标光学图像及典型角度下的真实图像和仿真图像

Figure 6. Optical images of SAR targets and the corresponding simulated and real SAR images under typical azimuths and depressions

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图 7 SOC测试集的混淆矩阵

Figure 7. The confusion matrix of the SOC test set

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图 8 EOC测试集的混淆矩阵

Figure 8. The confusion matrixes of the EOC test set

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图 9 使用t-SNE进行特征可视化

Figure 9. Visualization of extracted features using t-SNE

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图 10 LRP可视化真实数据训练得到的模型决策依据

Figure 10. Using LRP to visualize the decision basis of the model trained by the real data

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图 11 CLRP可视化真实数据训练得到的模型决策依据

Figure 11. Using CLRP to visualize the decision basis of the model trained by the real data

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图 12 LRP和CLRP可视化仿真数据训练得到的模型决策依据

Figure 12. Using LRP and CLRP to visualize the decision basis of the model trained by the simulated data

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图 13 域适应前模型对T72图像的LRP及CLRP解释

Figure 13. The predicted results of the T72 images by the model before domain adaptation and the corresponding explanations generated by LRP and CLRP

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图 14 域适应前后模型对T72真实图像的预测结果及CLRP解释

Figure 14. The predicted results of the real T72 image by the model before and after domain adaptation and the corresponding explanations generated by CLRP

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表 1 数据集

Table 1. Dataset

数据集	目标类别	俯仰角	数量
源域(仿真)训练集	BMP2	17°, 15°	3462
	BTR70	17°, 15°	3462
	T72	17°, 15°	3462
源域(仿真)测试集	BMP2	17°, 15°	864
	BTR70	17°, 15°	864
	T72	17°, 15°	864
目标域(真实)训练集	BMP2	17°	233
	BTR70	17°	233
	T72	17°	233
目标域(真实)测试集 (SOC和EOC)	BMP2	15°, 17°	1052
	BTR70	15°	196
	T72	15°, 17°, 30°	5906

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表 2 SOC数据集

Table 2. SOC dataset

目标类别	目标型号	训练集		测试集
目标类别	目标型号	俯仰角	数量	俯仰角	数量
BMP2	9563	17°	233	15°	195
BTR70	C71	17°	233	15°	196
T72	132	17°	233	15°	196

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表 3 EOC-1测试集(大俯仰角)

Table 3. EOC-1 test set (large depression variation)

目标类别	目标型号	俯仰角	数量
T72	A64	30°	288

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表 4 EOC-2测试集(配置变化)

Table 4. EOC-2 test set (configuration variant)

目标类别	目标型号	俯仰角	数量
T72	S7	15°, 17°	419
	A32	15°, 17°	572
	A62	15°, 17°	573
	A63	15°, 17°	573
	A64	15°, 17°	573

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表 5 EOC-3测试集(版本变化)

Table 5. EOC-3 test set (version variant)

目标类别	目标型号	俯仰角	数量
BMP2	9566	15°, 17°	428
BMP2	C21	15°, 17°	429
	812	15°, 17°	426
	A04	15°, 17°	573
T72	A05	15°, 17°	573
	A07	15°, 17°	573
	A10	15°, 17°	567

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表 6 网络训练过程中参数设置

Table 6. Parameters for the model training procedure

名称	参数值
batch size	32
优化器	SGD
初始学习率${\text{l}}{{\text{r}}_{\text{0}}}$	0.01
GRL参数$\lambda $	1
惩罚因子$\gamma $	1
epoch	500
iteration/epoch	35

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表 7 结合不同背景仿真SAR图像的消融实验结果

Table 7. Results of ablation experiments with simulation SAR images of different backgrounds

仿真背景	方法		准确率(%)
仿真背景	MK-MMD	域对抗训练	准确率(%)
①	×	×	28.00±1.39
	×	√	47.53±1.39
	√	×	35.48±0.52
	√	√	43.44±2.22
②	×	×	73.20±1.38
	×	√	77.40±2.18
	√	×	85.20±1.19
	√	√	87.03±1.29
③	×	×	65.07±0.52
	×	√	65.19±2.89
	√	×	73.57±0.62
	√	√	77.29±1.58
全部应用	×	×	74.79±1.35
	×	√	83.99±1.19
	√	×	85.85±2.33
	√	√	90.43±0.95

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表 8 使用不同仿真背景数据时，各方法在SOC测试集上的分类准确率对比

Table 8. Comparison of the classification accuracy on the SOC test set when using different methods with simulated data under different backgrounds

训练方法	准确率(%)
训练方法	全部仿真背景	仿真背景①	仿真背景②	仿真背景③
CNN(仅仿真训练集)	75.81	27.77	73.59	59.63
CDAN^[19]	76.83	41.74	77.85	72.23
DSAN^[14]	84.84	38.33	88.08	69.67
本文域适应方法	90.29	41.91	88.76	78.36

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