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

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

*此作品的通讯作者

科研成果: 期刊稿件文章同行评审

160 引用 (Scopus)

摘要

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.

源语言英语
页(从-至)1912-1925
页数14
期刊IEEE Transactions on Neural Networks and Learning Systems
35
2
DOI
出版状态已出版 - 1 2月 2024

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Zhang, Y., Li, W., Zhang, M., Wang, S., Tao, R., & Du, Q. (2024). Graph Information Aggregation Cross-Domain Few-Shot Learning for Hyperspectral Image Classification. IEEE Transactions on Neural Networks and Learning Systems, 35(2), 1912-1925. https://doi.org/10.1109/TNNLS.2022.3185795