Context-Enhanced and Scale-Contrastive Learning for Remote Sensing Imagery Understanding

Self-supervised learning (SSL) has shown strong potential for learning representations from unlabeled data, yet its direct application to remote sensing (RS) imagery remains challenging due to complex backgrounds and severe target-scale variations. To address these challenges, we propose Context-Enhanced Semantic Contrast (CESC), a self-supervised pre-training framework tailored for RS imagery. CESC integrates masked reconstruction, explicit contextual enhancement, and cross-scale semantic contr