Cascade Network for Hyperspectral Image Classification

Convolutional neural network (CNN) is one of the most powerful tools to deal with computer vision tasks such as hyperspectral image (HSI) classification. While many studies using CNN focus on classification precision, few of them pay attention to the model size and running time. Some studies focus on lightweight neural networks for traditional RGB image processing tasks and achieve fantastic results, but none of them are designed for hyperspectral image processing. In this paper, a novel lightweight neural network designed for hyperspectral image classification is proposed to do fast HSI processing while maintaining high classification precision. The network uses the idea of feature reuse to reduce parameter size and improve convergence. Expansion convolution is adopted to overcome the defects which are brought by parameter reduction. The experiments show that the proposed network has SOTA level classification accuracy while maintaining high processing speed.