Dual Graph Cross-domain Few-shot Learning for Hyperspectral Image Classification

A Dual Graph Cross-domain Few-shot Learning (DG-CFSL) framework is proposed, trying to make up for the above shortcomings by combining Few-shot Learning (FSL) with domain alignment. Both SD with all label samples and TD with a few label samples are implemented for FSL episodic training. Meanwhile, Intra-domain Distribution Extraction block (IDE-block) is designed to characterize and aggregate the intra-domain non-local relationships. Furthermore, feature- and distribution-level cross-domain graph alignments are used to mitigate the impact of domain shift on FSL.