微波视觉与SAR图像智能解译

徐丰; 金亚秋

doi:10.12000/JR23225

微波视觉与SAR图像智能解译

DOI: 10.12000/JR23225

徐丰^,,
金亚秋

电磁波信息科学教育部重点实验室复旦大学上海 200433

基金项目: 国家自然科学基金(61991422)

详细信息

作者简介:
徐　丰，博士，教授，主要研究方向为SAR图像解译、电磁散射建模、人工智能等

金亚秋，博士，教授，中国科学院院士，主要研究方向为复杂自然环境与目标电磁散射辐射传输、空间微波遥感和计算电磁

通讯作者:
徐丰 fengxu@fudan.edu.cn

责任主编：仇晓兰 Corresponding Editor: QIU Xiaolan
中图分类号: TN957.51
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出版历程
- 收稿日期: 2023-11-21
- 修回日期: 2024-01-07
- 网络出版日期: 2024-01-30
- 刊出日期: 2024-04-28

Microwave Vision and Intelligent Perception of Radar Imagery

XU Feng^,,
JIN Yaqiu

Key Lab for Information Science of Electromagnetic Waves (MoE), Fudan University, Shanghai 200433, China

Funds: The National Natural Science Foundation of China (61991422)

More Information

Corresponding author: XU Feng, fengxu@fudan.edu.cn

摘要

摘要: 高分辨率雷达成像技术和人工智能、大数据技术的快速发展，有力促进了雷达图像智能解译技术的进步。由于雷达传感器本身的特殊性和电磁散射成像物理的复杂性，雷达图像的解译缺乏光学图像的直观性，准确迅速识别分类的需求对雷达图像解译提出了迫切的挑战。在借鉴人脑光视觉感知机理和计算机视觉图像处理相关技术基础上，进一步融合电磁散射物理规律及其雷达成像机理，我们提出发展微波域雷达图像解译的“微波视觉”的新交叉领域研究。该文介绍微波视觉的概念与内涵，提出微波视觉认知模型，阐述其基础理论问题与技术路线，最后介绍了作者团队在相关问题上的初步研究进展。
- 合成孔径雷达(SAR) /
- 雷达成像 /
- 电磁散射 /
- 目标识别 /
- 微波视觉 /
- 语义电磁散射建模 /
- 物理智能 /
- 逆问题 /
- 视觉感知
Abstract: With the rapid development of high-resolution radar imaging technology, artificial intelligence, and big data technology, remarkable advancements have been made in the intelligent interpretation of radar imagery. Despite growing demands, radar image intrpretation is now facing various technical challenges mainly because of the particularity of the radar sensor itself and the complexity of electromagnetic scattering physical phenomena. To address the problem of microwave radar imagery perception, this article proposes the development of the cross-disciplinary microwave vision research, which further integrates electromagnetic physics and radar imaging mechanism with human brain visual perception principles and computer vision technologies. This article discusses the concept and implication of microwave vision, proposes a microwave vision perception model, and explains its basic scientific problems and technical roadmaps. Finally, it introduces the preliminary research progress on related issues achieved by the authors’ group.
- Synthetic Aperture Radar (SAR) /
- Radar imaging /
- Electromagnetic scattering /
- Target recognition /
- Microwave vision /
- Semantic electromagnetics /
- Physical intelligence /
- Inverse problem /
- Visual perception

HTML全文

图 1 微波视觉概念示意图

Figure 1. Concept of microwave vision

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图 2 同一目标在光学图像和SAR图像上的特点对比

Figure 2. Comparision of the characteristics of the same targets in optical and SAR images

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图 3 光学视觉认知规律在微波图像域将失效

Figure 3. Visual perceptual rules become invalid in the microwave image domain

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图 4 微波视觉与光学视觉的关系

Figure 4. Relationship between microwave vision and optical vision

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图 5 微波视觉认知模型

Figure 5. The perception model of microwave vision

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图 6 微波视觉的可能实现方法

Figure 6. Possible implementations of microwave vision

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图 7 语义电磁散射基本属性与实现途径

Figure 7. Basic properties and implementations of semantic electromagnetic scattering model

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图 8 物理先验与神经网络的不同层次融合

Figure 8. Fusion of physical priors and neural networks at different levels

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图 9 智能体、模拟器及专家之间的交互模式

Figure 9. Interaction modes among agents, simulators, and experts

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图 10 相干散射子基元字典及SAR图像语义表征模式^[39]

Figure 10. Primitive Scatterer Dictionary (PSD) and semantic representation of SAR images^[39]

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图 11 仿真辅助数据增广对于目标分类性能的提升

Figure 11. Performance improvements for target classification with simulation-assisted data augmentation

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图 12 SAR图像因果模型

Figure 12. Causal model of SAR images

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图 13 SAR-NeRF原理与等价计算图^[50]

Figure 13. SAR-NeRF principle and the equivalent computation graph^[50]

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图 14 可微SAR渲染器(DSR)

Figure 14. Differentiable SAR Renderer (DSR)

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图 15 智能体与模拟器交互式反演架构^[53]

Figure 15. Interactive inversion architecture between agents and simulators^[53]

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图 16 智能体与专家交互的人机协同学习模式^[38]

Figure 16. Human-machine collaborative learning mode with interactions between agents and experts^[38]

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表 1 光学图像和SAR图像对比

Table 1. Comparison between optical and SAR images

图像特性		光学图像	SAR图像
物理特性	波段	可见光波段	微波波段
	探测方式	外界光源、被动接收	主动辐射、后向散射
	反射/散射形态	连续、面状	离散、点状
成像机制	聚焦机制	真实孔径	相干合成孔径
	随机噪声	加性噪声	乘性相干斑
	投影方式	透视投影	斜距投影
	投影方向	俯仰角-方位角	距离向-方位向
图像形态	图像畸变效应	透视效应，分辨率与距离成正比	收缩、叠掩、倒置，分辨率与距离无关
	目标与场景呈现方式	自然图像：人眼视角、大目标小背景	遥感图像：鹰眼视角、大背景小目标
	数据形式	颜色、强度	相位、幅度、极化

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表 2 微波视觉认知模型中的基本概念

Table 2. Notations of the perception model of microwave vision

概念	定义	举例
目标语义知识	$ k{\text{～}}{P}_{k}\left(k\right)\in {\mathbb{C}}^{{N}_{k}} $	目标型号：T72, BTR60 ···
目标多样性	$ d{\text{～}}{P}_{d}\left(d\right)\in {\mathbb{C}}^{{N}_{d}} $	细节变化、个体差异、背景环境···
目标物理信息	$ x{\text{～}}{P}_{x}\left(x\right)\in {\mathbb{R}}^{{N}_{x}} $	目标几何模型、表面材质···
观测数据	$ y{\text{～}}{P}_{y}\left(y\right)\in {\mathbb{R}}^{{N}_{y}} $	SAR图像
观测配置	$ \theta $	波段、入射角、分辨率···
观测噪声	$ \delta $	传感器噪声、测量误差、模型误差···

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