An Unsupervised Framework for Radar Sounder Signal Segmentation Based on
Enhanced Self-supervised Transformers
Abstract
We utilize the radar sounder data (radargrams) for automatic subsurface
target identifications. We develop an enhaced unsupervised framework
(based of selfi-supervised Vision Transformers) for the semantic
segmentation of radar sounder signal.
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