CNN-Transformer and Channel-Spatial Attention based network for hyperspectral image classification with few samples.
Journal:
Neural networks : the official journal of the International Neural Network Society
Published Date:
Feb 22, 2025
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.