Explainable, Physics Aware, Trustworthy AI Paradigm Shift for Synthetic Aperture Radar. (arXiv:2301.03589v1 [eess.IV])

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.