Data Augmentation and Spatial-Spectral Residual Framework for Hyperspectral Image Classification Using Limited Samples

Hyperspectral image classification is a prominent topic in many remote sensing applications, but the limited number of manually annotated samples leads to performance bottlenecks. To resolve this issue, a data augmentation and spatial-spectral residual framework is proposed for hyperspectral image classification using limited samples. Firstly, an unsupervised pseudo-sample generation method is proposed to augment the sample set, and the generalization capability of the model is improved by mixup operations. Then, to adequately extract the spatial-spectral features of hyperspectral images, a spatial-spectral residual framework is designed to improve the classification performance of the model. The qualitative and quantitative experiments were carried out on Indian Pines dataset to validate the effectiveness of the model.