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Bridging a Gap in SAR-ATR: Training on Fully Synthetic and Testing on Measured Data
2021
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Obtaining measured Synthetic Aperture Radar (SAR) data for training Automatic Target Recognition (ATR) models can be too expensive (in terms of time and money) and complex of a process in many situations. In response, researchers have developed methods for creating synthetic SAR data for targets using electro-magnetic prediction software, which is then used to enrich an existing measured training dataset. However, this approach relies on the availability of some amount of measured data. In this
doi:10.1109/jstars.2021.3059991
fatcat:2e6whpnw2ba7vhhjnn3mwyi47y