DF2Net: Differential Feature Fusion Network for Hyperspectral Image Classification

Recently, hybrid networks, combining graph convolutional networks (GCNs) and convolutional neural networks (CNNs) into a unified framework, have garnered significant attention in hyperspectral image (HSI) classification. However, existing hybrid networks have the following limitations: 1) Existing methods primarily utilize simple fusion strategies such as concatenation or direct addition, resulting in the ineffective utilization of advantageous features. 2) Traditional GCNs only consider the relationship between pairs of vertices, limiting their ability to capture complex high-order and long-range correlations. In this work, a novel differential feature fusion network (DF2Net) is proposed for HSI classification. Specifically, DF2Net utilizes two subnetworks to learn features at different abstraction levels: the spectral-spatial hypergraph convolutional network (S2HGCN) for capturing complex high-order and long-range correlations, and the spectral-spatial convolution network (S2CN) for pixel-level local information extraction. Subsequently, we introduce an advantageous feature differential enhancement fusion (AFDEF) module, in which mutual enhancement of advantageous features from different network structures is performed, thereby improving the classification robustness of different regions in HSI. The experiments on four HSI benchmark datasets demonstrate that our DF2Net exhibits superior advantages over state-of-the-art models, particularly when the training samples are limited.