Cross-Domain Few-Shot Learning with Spectral-Spatial Split-Attention for Hyperspectral Image Classification

Hyperspectral image classification (HSIC) is a pivotal technology in hyperspectral remote sensing, playing a widespread role in remote sensing applications. However, the limited number of labeled samples has always made hyperspectral image classification difficult. In response to this issue, researchers have delved into cross-domain classification studies. Moreover, significant progress on cross-domain HSIC has been made in recent years. Nevertheless, existing methods exhibit shortcomings, including inadequate exploitation of spectral and spatial information and a slow training speed, rendering them unsuitable for downstream application tasks. To address these challenges, this paper introduces a model of cross-domain few-shot learning with spectral-spatial split attention(S³A-CFSL). Channel attention and split attention are presented to emphasize effective spectral and spatial information for HSIC adaptively. Additionally, the ResNet variant, called ResNeSt, is employed to expedite the training speed of the model. Experimental results demonstrate notable enhancements in the proposed method's classification accuracy and model training speed across two public datasets.