Graph-Based Domain Adaptation Few-Shot Learning for Hyperspectral Image Classification

Due to a lack of labeled samples, deep learning methods generally tend to have poor classification performance in practical applications. Few-shot learning (FSL), as an emerging learning paradigm, has been widely utilized in hyperspectral image (HSI) classification with limited labeled samples. Howe...

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Bibliographic Details
Published in:Remote sensing (Basel, Switzerland) Switzerland), 2023-02, Vol.15 (4), p.1125
Main Authors: Xu, Yanbing, Zhang, Yanmei, Yue, Tingxuan, Yu, Chengcheng, Li, Huan
Format: Article
Language:English
Subjects:
Adaptation
Artificial intelligence
attention mechanism
Classification
Comparative analysis
Datasets
Deep learning
domain adaptation
Domains
Feature extraction
few-shot classification
few-shot leaning (FSL)
graph construction
Hyperspectral imaging
Image classification
Image processing
Machine learning
Methods
Metric space
Multispectral photography
Neighborhoods
Neural networks
Remote sensing
Sample size
Semantics
Success
Teaching methods
Technology application
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Summary:Due to a lack of labeled samples, deep learning methods generally tend to have poor classification performance in practical applications. Few-shot learning (FSL), as an emerging learning paradigm, has been widely utilized in hyperspectral image (HSI) classification with limited labeled samples. However, the existing FSL methods generally ignore the domain shift problem in cross-domain scenes and rarely explore the associations between samples in the source and target domain. To tackle the above issues, a graph-based domain adaptation FSL (GDAFSL) method is proposed for HSI classification with limited training samples, which utilizes the graph method to guide the domain adaptation learning process in a uniformed framework. First, a novel deep residual hybrid attention network (DRHAN) is designed to extract discriminative embedded features efficiently for few-shot HSI classification. Then, a graph-based domain adaptation network (GDAN), which combines graph construction with domain adversarial strategy, is proposed to fully explore the domain correlation between source and target embedded features. By utilizing the fully explored domain correlations to guide the domain adaptation process, a domain invariant feature metric space is learned for few-shot HSI classification. Comprehensive experimental results conducted on three public HSI datasets demonstrate that GDAFSL is superior to the state-of-the-art with a small sample size.
ISSN:2072-4292
2072-4292
DOI:10.3390/rs15041125