Bi-Kernel Graph Neural Network with Adaptive Propagation Mechanism for Hyperspectral Image Classification

Abstract

Graph neural networks (GNNs) have been widely applied for hyperspectral image (HSI) classification, due to their impressive representation ability. It is well-known that typical GNNs and their variants work under the assumption of homophily, while most existing GNN-based HSI classification methods neglect the heterophily that is widely present in the constructed graph structure. To deal with this problem, a homophily-guided Bi-Kernel Graph Neural Network (BKGNN) is developed for HSI classification. In the proposed BKGNN, we estimate the homophily between node pairs according to a learnable homophily degree matrix, which is then applied to change the propagation mechanism by adaptively selecting two different kernels to capture homophily and heterophily information. Meanwhile, the learning process of the homophily degree matrix and the bi-kernel feature propagation process are trained jointly to enhance each other in an end-to-end fashion. Extensive experiments on three public data sets demonstrate the effectiveness of the proposed method.