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

30 Jun 2022IEEE Transactions on Neural Networks and Learning Systems 2022 6archive 2025-07-28

Yuxiang Zhang, Wei Li, Mengmeng Zhang, Shuai Wang, Ran Tao, Qian Du

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: https://github.com/YuxiangZhang-BIT/IEEE_TNNLS_Gia-CFSL.

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YuxiangZhang-BIT/IEEE_TNNLS_Gia-CFSL mentioned in paperpytorch report

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Tasks

Cross-Domain Few-Shot Domain Adaptation Few-Shot Image Classification Few-Shot Learning Hyperspectral Image Classification Image Classification cross-domain few-shot learning image-classification

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Hyperspectral Image Classification	Indian Pines	DCFSL	OA@15perclass	77.45±1.78	#7 of 34	Archive leaderboard	report
Hyperspectral Image Classification	Kennedy Space Center	DCFSL	OA@15perclass	97.59±1.03	#3 of 14	Archive leaderboard	report
Hyperspectral Image Classification	Pavia University	DCFSL	OA@15perclass	90.71±0.56	#4 of 33	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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