ReSC-net: Hyperspectral Image Classification Based on Attention-Enhanced Residual Module and Spatial-Channel Attention

Hyperspectral image (HSI) classification is a key technique in remote sensing. Despite the increasing availability of high-quality HSI data, obtaining a large number of labeled samples remains challenging in certain cases. Consequently, HSI classification often faces the issue of insufficiently labeled samples. To address this challenge, sample augmentation techniques can be used to generate additional training samples. However, because of the significant differences between hyperspectral images and ordinary natural images, some augmentation techniques are not suitable for hyperspectral classification scenarios. In this article, considering phenomena such as spectral aliasing in hyperspectral image classification and imaging processes, we propose a novel online augmentation technique for hyperspectral samples. During training, we apply random gains to the center pixel of labeled samples to increase the number of usable samples. Additionally, since augmented samples may still be insufficient, using overly complex networks can lead to overfitting. Therefore, we introduce a hyperspectral image classification network called attention-enhancing residual and spatial-channel attention-based network (ReSC-net). In ReSC-net, we observe that the spatial dimension of hyperspectral blocks is much smaller than the channel dimension, and the limited sample size can lead to overfitting when using complex networks. Thus, we propose a channel attention-enhanced residual module to extract low-level features. Furthermore, ReSC-net introduces new spatial-channel attention to further optimize the extracted deep features for better classification. We conduct experiments on four commonly used HSI datasets. The experimental results demonstrate that our algorithm achieves favorable results on multiple HSI classification evaluation metrics.