Short and Long Range Graph Convolution Network for Hyperspectral Image Classification

Abstract

Nowadays, graph convolution networks are getting more and more attention in the field of hyperspectral image classification. The graph convolution can be divided into long-range and short-range graph convolution (GConv). However, the two graph convolutions cannot acquire global and local features at the same time, making the node features may not be accurate enough. Therefore, we propose a novel graph convolution approach, called short and long range graph convolution (SLGConv), which combines the advantages of long-range and short-range GConv. SLGConv can extract long-range (global) and short-range (local) spatial-spectral features, eliminating the disadvantages of each of long-range and short-range graph convolution. Furthermore, SLGConv can ensure that the features of nodes are not smoothed in the convolution process. Then, three layers of SLGConv are used to form the short and long range graph convolution network (SLGCN) for hyperspectral image classification. Experiments on three HSI datasets indicate that the SLGCN can obtain better classification performance when compared with seven state-of-the-art methods.