计算机科学
人工智能
判别式
模式识别(心理学)
域适应
分类
图形
机器学习
高光谱成像
深度学习
领域(数学分析)
上下文图像分类
图像(数学)
分类器(UML)
数学
理论计算机科学
数学分析
作者
Yanbing Xu,Yanmei Zhang,Tingxuan Yue,Chengcheng Yu,Huan Li
出处
期刊:Remote Sensing
[Multidisciplinary Digital Publishing Institute]
日期:2023-02-18
卷期号:15 (4): 1125-1125
摘要
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.
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