Dual-Branch Residual Network for Cross-Domain Few-Shot Hyperspectral Image Classification with Refined Prototype
Journal:
arXiv
Published Date:
Apr 27, 2025
Abstract
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.
