提出双分支网络与优化原型，提升跨域少量样本高光谱分类精度
Dual-Branch Residual Network for Cross-Domain Few-Shot Hyperspectral Image Classification with Refined Prototype
- 双分支结构并行提取空间与光谱特征，增强表征能力
- 引入正则项生成更鲁棒的原型，提升小样本下分类性能
- 通过核概率匹配缓解传感器与环境差异带来的域偏移
卷积神经网络（CNN）在高光谱图像（HSI）分类中表现良好，但其3D卷积结构导致计算成本高，且在少样本场景下泛化能力有限。传感器差异和环境变化引起的域偏移进一步影响跨数据集适应性。基于度量的少样本学习（FSL）原型网络虽可缓解此问题，但其性能对原型质量敏感，尤其在样本稀疏时表现下降。为此，本文提出一种双分支残差网络，通过并行分支融合空间与光谱特征。同时，引入正则项以获取更稳健的优化原型，并采用核概率匹配策略对齐源域与目标域特征，缓解域偏移。在四个公开可用的HSI数据集上的实验表明，所提方法优于现有技术。
原文摘要 · Abstract (English)
Convolutional neural networks (CNNs) are effective for hyperspectral image (HSI) classification, but their 3D convolutional structures introduce high computational costs and limited generalization in few-shot scenarios. Domain shifts caused by sensor differences and environmental variations further hinder cross-dataset adaptability. Metric-based few-shot learning (FSL) prototype networks mitigate this problem, yet their performance is sensitive to prototype quality, especially with limited samples. To overcome these challenges, a dual-branch residual network that integrates spatial and spectral features via parallel branches is proposed in this letter. Additionally, more robust refined prototypes are obtained through a regulation term. Furthermore, a kernel probability matching strategy aligns source and target domain features, alleviating domain shift. Experiments on four publicly available HSI datasets illustrate that the proposal achieves superior performance compared to other methods.
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