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12 April 2021 An analysis of sparse image reconstruction quality of three-dimensionally focused synthetic aperture radar data
Paul Sotirelis, Sean Gilmore
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Proceedings Volume 11728, Algorithms for Synthetic Aperture Radar Imagery XXVIII; 117280E (2021) https://doi.org/10.1117/12.2587968
Event: SPIE Defense + Commercial Sensing, 2021, Online Only
Abstract
We present an analysis of image reconstruction quality that includes the use of traditional and deep-learning quality metrics for sparse reconstructions of three-dimensionally (3D) focused synthetic aperture radar (SAR) data. A major goal of our analysis is to explore the usefulness of various metrics to demonstrate their utility in 3D focused scenarios. We make use of synthetic prediction to help fully span the large parameter space of a two-dimensional cross-range aperture. The analysis including the synthetic prediction will help guide future measurements of scale models in our compact radar range.1
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Paul Sotirelis and Sean Gilmore "An analysis of sparse image reconstruction quality of three-dimensionally focused synthetic aperture radar data", Proc. SPIE 11728, Algorithms for Synthetic Aperture Radar Imagery XXVIII, 117280E (12 April 2021); https://doi.org/10.1117/12.2587968
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KEYWORDS
Image analysis
Image quality
Image restoration
Synthetic aperture radar
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