A self-supervised Vision Transformer trained on SETHI radar patches, without any MSTAR labels, gives 95.9% few-shot classification accuracy on MSTAR using k-NN with ten labels per class.
Widely employed to monitor various activities, SAR plays a crucial role in tracking urban development [2], assessing biomass changes [3], and detect- ing ships [4], for example
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General Feature Extraction In SAR Target Classification: A Contrastive Learning Approach Across Sensor Types
A self-supervised Vision Transformer trained on SETHI radar patches, without any MSTAR labels, gives 95.9% few-shot classification accuracy on MSTAR using k-NN with ten labels per class.