SAR图像目标识别的可解释性问题探讨

郭炜炜; 张增辉; 郁文贤; 孙效华

doi:10.12000/JR20059

SAR图像目标识别的可解释性问题探讨

doi: 10.12000/JR20059

1.
同济大学数字创新中心上海 200092
2.
上海交通大学智能探测与识别上海市重点实验室上海 200240

基金项目: 国家自然科学基金联合基金(U1830103)

详细信息

作者简介:
郭炜炜(1983–)，男，江苏南通人，博士，分别于2005，2007，2014年获得国防科技大学信息工程学士，信息与通信专业硕士和博士学位。2008年—2010年在英国Queen Mary，University of London联合培养，2014年12月至2018年6月在上海交通大学电子信息与电气工程学院从事博士后研究工作，2018年12月至今为同济大学设计创意学院助理教授。研究方向为遥感图像理解、模式识别与机器学习、人机交互等。E-mail: weiweiguo@tongji.edu.cn

张增辉(1980–)，男，山东金乡人，博士，分别于2001年、2003年和2008年在国防科技大学获得应用数学、计算数学、信息与通信工程专业学士、硕士和博士学位。2008年6月至2013年7月，为国防科技大学数学与系统科学系讲师；2014年2月至今，为上海交通大学电子信息与电气工程学院副研究员。研究方向为SAR图像解译、雷达信号处理等。E-mail: zenghui.zhang@sjtu.edu.cn

郁文贤(1964–)，男，上海松江人，博士，教授，博士生导师，上海交通大学讲席教授，教育部长江学者特聘教授，上海市领军人才。现为上海交通大学信息技术与电气工程研究院院长，北斗导航与位置服务上海市重点实验室主任，智能探测与识别上海市高校重点实验室主任。研究方向为遥感信息处理、多源融合导航定位、目标检测识别等。E-mail: wxyu@sjtu.edu.cn

孙效华(1972–)，女，河南安阳人，麻省理工学院设计与计算专业硕士与博士，教授，博士生导师，同济大学设计创意学院副院长。曾在MIT CECI、MIT媒体实验室、FXPAL、IBM研究院、美国克拉克森大学等机构从事研究与教学。研究方向为人机智能交互与共融、人-机器人交互HRI、可视分析等。E-mail: xsun@tongji.edu.cn

通讯作者:
张增辉 zenghui.zhang@sjtu.edu.cn

责任主编：邹焕新 Corresponding Editor: ZOU Huanxin
¹ https://pair-code.github.io/saliency/
中图分类号: TN957.51
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出版历程
- 收稿日期: 2020-05-11
- 修回日期: 2020-06-17
- 网络出版日期: 2020-06-01

Perspective on Explainable SAR Target Recognition

1.
Center of Digital Innovation, Tongji University, Shanghai 200092, China
2.
Shanghai Key Lab of Intelligent Sensing and Recognition, Shanghai Jiaotong University, Shanghai 200240, China

Funds: The National Natural Science Foundation of China(U1830103)

More Information

Corresponding author: ZHANG Zenghui, zenghui.zhang@sjtu.edu.cn

摘要

摘要: 合成孔径雷达(SAR)图像目标识别是实现微波视觉的关键技术之一。尽管深度学习技术已被成功应用于解决SAR图像目标识别问题，并显著超越了传统方法的性能，但其内部工作机理不透明、解释性不足，成为制约SAR图像目标识别技术可靠和可信应用的瓶颈。深度学习的可解释性问题是目前人工智能领域的研究热点与难点，对于理解和信任模型决策至关重要。该文首先总结了当前SAR图像目标识别技术的研究进展和所面临的挑战，对目前深度学习可解释性问题的研究进展进行了梳理。在此基础上，从模型理解、模型诊断和模型改进等方面对SAR图像目标识别的可解释性问题进行了探讨。最后，以可解释性研究为切入点，从领域知识结合、人机协同和交互式学习等方面进一步讨论了未来突破SAR图像目标识别技术瓶颈有可能的方向。
- 合成孔径雷达 /
- 自动目标识别 /
- 深度学习 /
- 可解释性 /
- 可解释机器学习
Abstract: SAR Automatic Target Recognition (ATR) is a key task in microwave remote sensing. Recently, Deep Neural Networks (DNNs) have shown promising results in SAR ATR. However, despite the success of DNNs, their underlying reasoning and decision mechanisms operate essentially like a black box and are unknown to users. This lack of transparency and explainability in SAR ATR pose a severe security risk and reduce the users’ trust in and the verifiability of the decision-making process. To address these challenges, in this paper, we argue that research on the explainability and interpretability of SAR ATR is necessary to enable development of interpretable SAR ATR models and algorithms, and thereby, improve the validity and transparency of AI-based SAR ATR systems. First, we present recent developments in SAR ATR, note current practical challenges, and make a plea for research to improve the explainability and interpretability of SAR ATR. Second, we review and summarize recent research in and practical applications of explainable machine learning and deep learning. Further, we discuss aspects of explainable SAR ATR with respect to model understanding, model diagnosis, and model improvement toward a better understanding of the internal representations and decision mechanisms. Moreover, we emphasize the need to exploit interpretable SAR feature learning and recognition models that integrate SAR physical characteristics and domain knowledge. Finally, we draw our conclusion and suggest future work for SAR ATR that combines data and knowledge-driven methods, human–computer cooperation, and interactive deep learning.
- SAR /
- Automatic Target Recognition (ATR) /
- Deep learning /
- Explainability and interpretability /
- Explainable machine learning

HTML全文

图 1 一个简单CNN分类器在T72 SAR图像的梯度-类激活映射(Grad-CAM^[6])

Figure 1. The results of a CNN classifier and the Grad-CAM map^[6]

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图 2 MSTAR T62光学图像与不同方位角下的SAR图像

Figure 2. The Optical image and SAR image samples of T62 tank at different azimuth angles in MSTAR dataset

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图 3 SAR目标识别面临的挑战

Figure 3. Challenges of SAR ATR

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图 4 可解释性学习

Figure 4. Explainable machine learning

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图 5 基于梯度系列方法的决策显著性^①

Figure 5. Decision saliency of the Gradient-based methods

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图 6 LIME示意图^[55]

Figure 6. Illustration of LIME^[55]

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图 7 SAR目标识别可解释性研究

Figure 7. Explainable SAR automatic target recognition

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图 8 SAR目标识别的样本决策重要性分析

Figure 8. Decision importance analysis for the SAR target recognition model

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图 9 物理知识引导的SAR特征学习网络

Figure 9. Physical model guided feature learning for SAR images

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表 1 典型的可解释性方法

Table 1. Typical methods for explainablitiy

解释的对象	模型依赖(Model-specific)	模型无关(Model-agnostic)
解释模型 Explain model	■激活最大化方法AM^[43,44] ■概念激活矢量TCAV^[45]	■知识蒸馏(Knowledge distilling)^[46] ■特征置换(Permutation)^[47]
解释样本 Explain sample	■基于梯度的方法Grad^[48], GuidedBP^[49], IntegratedGrad^[50], SmoothGrad^[51] ■特征扰动分析Perturbation^[52] ■层次相关传播LRP^[53] ■类激活映射CAM^[54], Grad-CAM^[6]	■基于局部代理模型的方法，如LIME^[55] ■基于实例的方法，如Influence function^[56]，Critic样本方法^[57] ■基于Shapley值的方法^[58]

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