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

By Yuxiang Zhang, Wei Li, Mengmeng Zhang, Shuai Wang, Ran Tao, Qian Du. IEEE Transactions on Neural Networks and Learning Systems, 2022

310 citations recorded by the source corpus.

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.

Subfields: Artificial Intelligence, Media Technology

View Paper: https://www.semanticscholar.org/paper/9404559e89f0c64d7e1bb204b51feb50fe60b836

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Dacheng Tao
2. Jing Zhang
3. Bo Du
4. Qiming Zhang
5. Yufei Xu
6. Di Wang
7. L. Zhang
8. Dalong Du

In Media Technology

1. Bo Du
2. Dacheng Tao
3. Qiming Zhang
4. Jing Zhang
5. L. Zhang
6. Di Wang
7. Yufei Xu
8. Liangpei Zhang

Citations in the corpus

• Advancing Plain Vision Transformer Toward Remote Sensing Foundation Model (2022, methodology): “[4], [5], [6], [7], [8], [9], [10] In the current remote sensing (RS) field, convolutional neural networks (CNNs) are the most commonly used models for extracting hierarchical multiscale visual features.”