Multidimensional Information Expansion and Processing Network for Hyperspectral Image Classification.

In recent years, deep learning (DL) has been extensively used in the hyperspectral image (HSI) classification. The representative method is the convolutional neural network (CNN). However, due to the limitations of its inherent network backbone, CNNs still easily fail to mine some important information about HSIs, such as the sequence attributes of spectral signatures. To deal with this problem and make full use of the spectral-spatial information of HSIs, we propose a novel network named multidimensional information expansion and processing network (MIEPN) for HSI classification, which is mainly composed of one information expansion module (IEM), one feature information expansion and extraction module (FEEM), and one vision transformer (ViT) module. Briefly speaking, IEM expands and fuses HSI information in a 3-D space, yet FEPM pays more attention to digging deeper information. After these, the extracted information is input into the ViT module for HSI classification. Experiments carried out on several typical datasets demonstrate that the proposed network MIEPN can provide competitive results compared to the other state-of-the-art CNN-based methods.