An attention-driven gobal reasoning network for hyperspectral image classification

Hyperspectral images (HSI) contain rich spectral information essential for real-world applications, but traditional methods struggle with limited training data and complexity. Convolutional neural networks (CNNs) also face challenges in capturing global features. This letter proposes a CNN-based model with a global reasoning module (GRM) to integrate local and global features effectively. A spectral–spatial feature extractor (depthwise and pointwise convolutions) captures local details, while a global reasoning layer models long-range relationships. Experiments on four public HSI datasets validate the model’s superior classification performance.