IEEE Geoscience and Remote Sensing Magazine - March 2023 - 8
Explainable,
Physics-Aware,
Trustworthy
Artificial Intelligence
A paradigm shift for synthetic aperture radar
MIHAI DATCU , ZHONGLING HUANG , ANDREI ANGHEL ,
JUANPING ZHAO , AND REMUS CACOVEANU
T
he recognition or understanding of the scenes observed
with a synthetic aperture radar (SAR) system requires a
broader range of cues beyond the spatial context. These encompass
but are not limited to the imaging geometry, imaging
mode, properties of the Fourier spectrum of the images,
or behavior of the polarimetric signatures. In this article,
we propose a change of paradigm for explainability in data
science for the case of SAR data to ground explainable artificial
intelligence (XAI) 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 the training process, and to learn or improve
high-complexity unknown or unformalized models from
the data.
First, we introduce a representation of the SAR system
with physical layers: 1) the instrument and platform, 2)
the imaging formation, and 3) scattering signatures and
objects that can be integrated with an AI model for hybrid
Digital Object Identifier 10.1109/MGRS.2023.3237465
Date of current version: 3 February 2023
8
2473-2397/23©2023IEEE
modeling. Successively, some illustrative examples are presented
to demonstrate how to achieve hybrid modeling for
SAR image understanding. The perspective of a trustworthy
model and supplementary explanations are discussed
later. Finally, we draw conclusions, and we deem that the
proposed concept has applicability to the entire class of
coherent imaging sensors and other computational imaging
systems.
MOTIVATION AND SIGNIFICANCE
Earth is facing unprecedented climatic, geomorphologic,
environmental, and anthropogenic changes that require
global-scale, long-term observation with Earth observation
(EO) sensors. SAR sensors, due to their observation
capability during day and night and independence of atmospheric
effects, are the only EO technology to ensure
global and continuous observations. Meanwhile, the SAR
observations of Sentinel-1 satellites in the frame of the
European Copernicus program are freely and openly accessible
worldwide. This is immensely enlarging SAR data
science and applicat ions , cover ing a mult itude of
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE MARCH 2023
https://orcid.org/0000-0002-3477-9687
https://orcid.org/0000-0003-2368-9229
https://orcid.org/0000-0003-3875-3238
https://orcid.org/0000-0002-1620-4580
https://orcid.org/0000-0003-4773-4610
IEEE Geoscience and Remote Sensing Magazine - March 2023
Table of Contents for the Digital Edition of IEEE Geoscience and Remote Sensing Magazine - March 2023
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