Journal
IEEE GEOSCIENCE AND REMOTE SENSING MAGAZINE
Volume 11, Issue 1, Pages 8-25Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/MGRS.2023.3237465
Keywords
Synthetic aperture radar; Radar polarimetry; Sensors; Scattering; Physical layer; Radar imaging; Imaging
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This article proposes a new paradigm for explainability in SAR data, using explainable data transformations and AI methods to gain a better understanding and knowledge feedback.
The 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.
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