CDEST: Class Distinguishability-Enhanced Self-Training Method for Adopting Pre-Trained Models to Downstream Remote Sensing Image Semantic Segmentation
Ming Zhang,
Xin Gu,
Ji Qi
et al.
Abstract:The self-supervised learning (SSL) technique, driven by massive unlabeled data, is expected to be a promising solution for semantic segmentation of remote sensing images (RSIs) with limited labeled data, revolutionizing transfer learning. Traditional ‘local-to-local’ transfer from small, local datasets to another target dataset plays an ever-shrinking role due to RSIs’ diverse distribution shifts. Instead, SSL promotes a ‘global-to-local’ transfer paradigm, in which generalized models pre-trained on arbitraril… Show more
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