LIRnet: Lightweight Hyperspectral Image Classification Based on Information Redistribution

Deep learning has received much attention in hyperspectral image (HSI) classification. However, most deep learning methods design relatively complex feature extraction and processing network modules for the characteristics of HSIs, which may not be necessary for relatively simple patch-based HSI classification tasks. The complex network structure and high feature channel dimension lead to large computational complexities, which limit the practical applicability of HSI. In this article, an elegant lightweight HSI classification-based information redistribution network (LIRnet) is proposed to separate and reaggregate the feature information to achieve feature information homogenization and extract discriminative feature information, respectively. The classification performance of LIRnet is better than that of existing methods on three different datasets in different scenarios, which proves its effectiveness. In addition, experiments on embedded devices verify the computational efficacy of LIRnet.