Advancing Multi-Scale Remote Sensing Analysis Through Self-Supervised Learning Fine-Tuning Strategies

This research focuses on improving the fine-tuning process of self-supervised learning models for remote sensing, particularly the Cross-Scale Masked Auto-Encoder (MAE). We tackle the challenges of intricate, multi-source imagery and present advancements in adapting the Cross-Scale MAE for diverse remote sensing environments. Our contributions include methods for handling complex dataset dimensions and semantic diversity, demonstrating the model’s adaptability and expanding its application scope in remote sensing.