CNN-Transformer and Channel-Spatial Attention based network for hyperspectral image classification with few samples

Abstract

Hyperspectral image classification is an important foundational technology in the field of Earth observation and remote sensing. In recent years, deep learning has achieved a series of remarkable achievements in this area. These deep learning-based hyperspectral image classifications typically require a large number of annotated samples to train the models. However, obtaining a large number of accurate annotated hyperspectral images for high-altitude or remote areas is usually extremely difficult. In this paper, we propose a novel algorithm, CTA-net, for hyperspectral classification with a small number of samples. First, we proposed a sample expansion scheme to generate a large number of new samples to alleviate the problem of insufficient samples. On this basis, we introduced a novel hyperspectral classification network. The network first utilizes a module based on CNN-Transformer to extract blocks of hyperspectral images, where CNN focuses primarily on local features, while the Transformer module focuses mainly on non-local features. Furthermore, a simple channel-spatial attention module is adopted to further optimize the features. We conducted experiments on multiple hyperspectral image datasets, and the experiments verified the effectiveness of our CTA-net.