DUAL GRAPH CROSS-DOMAIN FEW-SHOT LEARNING FOR HYPERSPECTRAL IMAGE CLASSIFICATION

Most domain adaptation (DA) methods focus on the case where the source data (SD) and target data (TD) with the same classes are obtained by the same sensor in cross-scene hyperspectral image (HSI) classification tasks. However, the classification performance is significantly reduced when there are new classes in TD. In addition, domain alignment is carried out based on local spatial information in most methods, rarely taking into account the non-local spatial information (non-local relationships) with strong correspondence. 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. Experimental results on two public HSI data sets demonstrate the effectiveness of the proposed method.