Dual-Branch Grouping Multiscale Residual Embedding U-Net and Cross-Attention Fusion Networks for Hyperspectral Image Classification

Due to the high cost and time-consuming nature of acquiring labelled samples of hyperspectral data, classification of hyperspectral images with a small number of training samples has been an urgent problem. In recent years, U-Net can train the characteristics of high-precision models with a small amount of data, showing its good performance in small samples. To this end, this paper proposes a dual-branch grouping multiscale residual embedding U-Net and cross-attention fusion networks (DGMRU_CAF) for hyperspectral image classification is proposed. The network contains two branches, spatial GMRU and spectral GMRU, which can reduce the interference between the two types of features, spatial and spectral. In this case, each branch introduces U-Net and designs a grouped multiscale residual block (GMR), which can be used in spatial GMRUs to compensate for the loss of feature information caused by spatial features during down-sampling, and in spectral GMRUs to solve the problem of redundancy in spectral dimensions. Considering the effective fusion of spatial and spectral features between the two branches, the spatial-spectral cross-attention fusion (SSCAF) module is designed to enable the interactive fusion of spatial-spectral features. Experimental results on WHU-Hi-HanChuan and Pavia Center datasets shows the superiority of the method proposed in this paper.