Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image Classification

Most domain adaptation (DA) methods in cross-scene hyperspectral image classification focus on cases where source data (SD) and target data (TD) with the same classes are obtained by the same sensor. However, the classification performance is significantly reduced when there are new classes in TD. In addition, domain alignment, as one of the main approaches in DA, is carried out based on local spatial information, rarely taking into account nonlocal spatial information (nonlocal relationships) with strong correspondence. A graph information aggregation cross-domain few-shot learning (Gia-CFSL) framework is proposed, intending to make up for the above-mentioned shortcomings by combining FSL with domain alignment based on graph information aggregation. SD with all label samples and TD with a few label samples are implemented for FSL episodic training. Meanwhile, intradomain distribution extraction block (IDE-block) and cross-domain similarity aware block (CSA-block) are designed. The IDE-block is used to characterize and aggregate the intradomain nonlocal relationships and the interdomain feature and distribution similarities are captured in the CSA-block. Furthermore, feature-level and distribution-level cross-domain graph alignments are used to mitigate the impact of domain shift on FSL. Experimental results on three public HSI datasets demonstrate the superiority of the proposed method. The codes will be available from the website:

Task Dataset Model Metric Name Metric Value Global Rank Benchmark
Hyperspectral Image Classification Indian Pines DCFSL OA@15perclass 77.45±1.78 # 6
Hyperspectral Image Classification Kennedy Space Center DCFSL OA@15perclass 97.59±1.03 # 3
Hyperspectral Image Classification Pavia University DCFSL OA@15perclass 90.71±0.56 # 4