The recognition or understanding of the scenes observed with a SAR system
requires a broader range of cues, beyond the spatial context. These encompass
but are not limited to: imaging geometry, imaging mode, properties of the
Fourier spectrum of the images or the behavior of the polarimetric signatures.
In this paper, we propose a change of paradigm for explainability in data
science for the case of Synthetic Aperture Radar (SAR) data to ground the
explainable AI for SAR. It aims to use explainable data transformations based
on well-established models to generate inputs for AI methods, to provide
knowledgeable feedback for training process, and to learn or improve
high-complexity unknown or un-formalized models from the data. At first, we
introduce a representation of the SAR system with physical layers: i)
instrument and platform, ii) imaging formation, iii) scattering signatures and
objects, that can be integrated with an AI model for hybrid modeling.
Successively, some illustrative examples are presented to demonstrate how to
achieve hybrid modeling for SAR image understanding. The perspective of
trustworthy model and supplementary explanations are discussed later. Finally,
we draw the conclusion and we deem the proposed concept has applicability to
the entire class of coherent imaging sensors and other computational imaging
systems.

本文提出了一种基于可解释数据转换的新范式，以生成 AI 方法的输入，提供知识反馈，从而从数据中学习或改进高复杂度未知或未形式化的模型，此方法被成功应用与合成孔径雷达数据的混合建模和图像理解。