Explainable, Physics Aware, Trustworthy AI Paradigm Shift for Synthetic Aperture Radar

TL;DR

This paper proposes a new paradigm for explainable AI in Synthetic Aperture Radar by integrating physical models with AI to enhance interpretability, trustworthiness, and hybrid modeling of SAR data.

Contribution

It introduces a physics-aware, explainable data transformation approach for SAR, combining physical layers with AI for improved understanding and model training.

Findings

Hybrid modeling demonstrates improved SAR image understanding.

Explainable transformations provide meaningful feedback for AI training.

The approach is applicable to other coherent imaging systems.

Abstract

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.