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Progress and Perspective on Physically Explainable Deep Learning for Synthetic Aperture Radar Image Interpretation（in English）
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摘要:
深度学习技术近年来在合成孔径雷达(SAR)图像解译领域发展迅速，但当前基于数据驱动的方法通常忽视了SAR潜在的物理特性，预测结果高度依赖训练数据，甚至违背了物理认知。深层次地整合理论驱动和数据驱动的方法在 SAR 图像解译领域尤为重要，数据驱动的方法擅长从大规模数据中自动挖掘新模式，对物理过程能起到有效的补充；反之，在数据驱动方法中加入可解释的物理模型能提升深度学习算法的透明度，并降低模型对标记样本的依赖。该文提出在SAR图像解译应用领域发展物理可解释的深度学习技术，从SAR信号、特性理解到图像语义和应用场景等多个维度开展研究，并结合物理机器学习提出了几种在SAR解译中融合物理模型和深度学习模型的研究思路，逐步发展可学习且可解释的智能化SAR图像解译新范式。在此基础上，该文回顾了近两三年在SAR图像解译相关领域中整合数据驱动深度学习和理论驱动物理模型的相关工作，主要聚焦信号特性理解和图像语义理解两大方向，并结合研究现状和其他领域的相关研究探讨了目前面临的挑战和未来可能的发展方向。
Abstract:Deep learning technologies have been developed rapidly in Synthetic Aperture Radar (SAR) image interpretation. The current data-driven methods neglect the latent physical characteristics of SAR; thus, the predictions are highly dependent on training data and even violate physical laws. Deep integration of the theory-driven and data-driven approaches for SAR image interpretation is of vital importance. Additionally, the data-driven methods specialize in automatically discovering patterns from a large amount of data that serve as effective complements for physical processes, whereas the integrated interpretable physical models improve the explainability of deep learning algorithms and address the data-hungry problem. This study aimed to develop physically explainable deep learning for SAR image interpretation in signals, scattering mechanisms, semantics, and applications. Strategies for blending the theory-driven and data-driven methods in SAR interpretation are proposed based on physics machine learning to develop novel learnable and explainable paradigms for SAR image interpretation. Further, recent studies on hybrid methods are reviewed, including SAR signal processing, physical characteristics, and semantic image interpretation. Challenges and future perspectives are also discussed on the basis of the research status and related studies in other fields, which can serve as inspiration.
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图 2 物理可解释的深度学习 SAR 图像解译应从多个维度开展研究，充分结合数据驱动和知识驱动的模型，逐步发展可学习且可解释的智能化图像解译新范式
Figure 2. The PXDL for SAR image interpretation is supposed to be carried out from multiple aspects, that deeply integrates the data-driven and knowledge-driven models to develop the novel learnable and explainable intelligent paradigm
图 3 SAR图像解译思路，①②③④⑤表示可以发展物理可解释深度学习方法的模块
Figure 3. The SAR image interpretation guideline, ①②③④⑤ are the potential modules to develop PXDL
图 2 The PXDL for SAR image interpretation is supposed to be carried out from multiple aspects, that deeply integrates the data-driven and knowledge-driven models to develop the novel learnable and explainable intelligent paradigm
图 3 The SAR image interpretation guideline. ① ② ③ ④ ⑤ are the potential modules to develop PXDL
图 4 The H/
$\alpha $ plane for full-polarized SAR data and the selected land-use and land-cover samples distributed in Ref. [50]
图 5 The unsupervised learning results of different polarized SAR images based on TFA and pol-extended TFA models[92]
图 8 The feature visualization of the unsupervised physics guided learning and supervised CNN classification on training and test set[100]
图 9 The amplitude images of convolution kernels in the first layer of CV-CNN based on ASC model initialization[106]
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