Hyperspectral Image Classification Based on Hybrid Depth-Wise Separable Convolution and Dual-Branch Feature Fusion Network

Recently, advancements in convolutional neural networks (CNNs) have significantly contributed to the advancement of hyperspectral image (HSI) classification. However, the problem of limited training samples is the primary obstacle to obtaining further improvements in HSI classification. The traditional methods relying solely on 2D-CNN for feature extraction underutilize the inter-band correlations of HSI, while the methods based on 3D-CNN alone for feature extraction lead to an increase in training parameters. To solve the above problems, we propose an HSI classification network based on hybrid depth-wise separable convolution and dual-branch feature fusion (HDCDF). The dual-branch structure is designed in HDCDF to extract simultaneously integrated spectral-spatial features and obtain complementary features via feature fusion. The proposed modules of 2D depth-wise separable convolution attention (2D-DCAttention) block and hybrid residual blocks are applied to the dual branch, respectively, further extracting more representative and comprehensive features. Instead of full 3D convolutions, HDCDF uses hybrid 2D-3D depth-wise separable convolutions, offering computational efficiency. Experiments are conducted on three benchmark HSI datasets: Indian Pines, University of Pavia, and Salinas Valley. The experimental results show that the proposed method showcases superior performance when the training samples are extremely limited, outpacing the state-of-the-art method by an average of 2.03% in the overall accuracy of three datasets, which shows that HDCDF has a certain potential in HSI classification.