Domain Dropout-Driven Prototype Enhancement Network for Cross-Domain Few-Shot Hyperspectral Change Detection (opens in new tab)

Deep learning (DL) has shown remarkable effectiveness in hyperspectral image change detection (HSI-CD). Cross-domain few-shot learning (CD-FSL) has emerged as an effective solution to label scarcity in the target domain by exploiting labeled source domain data. However, existing methods often suffer from the interference of domain-sensitive channels and the instability of class prototypes. To address these issues, this article proposes a domain dropout-driven prototype enhancement network (DD...