Multiscale Short and Long Range Graph Convolutional Network for Hyperspectral Image Classification

Abstract

Nowadays, graph convolution networks (GCNs) are getting more attention in hyperspectral image classification, and various algorithms based on GCNs have been proposed. However, because of hyperspectral images’ complex spatial texture information, the long-range graph convolution (GConv) and short-range GConv may cause inaccurate or over-smoothed feature extraction of some nodes. Thus, a multiscale short and long range graph convolution network (MSLGCN) is proposed for hyperspectral image classification. First, MSLGCN not only extracts spatial information of ground objects at different scales but also simultaneously captures global and local spectral features, which preserves objects’ fine boundaries. Then, the rich multiscale information is complementary, enabling the MSLGCN to take full advantage of texture structures of varying sizes. In addition, a method to determine the superpixel scale by the intrinsic properties of hyperspectral images is proposed to ensure that the segmentation boundary depicts the texture structure of the object accurately. Finally, the short-long graph convolution (SLGConv) is designed to fuse the advantages of global and local features, enabling the MSLGCN to extract accurate spatial-spectral features of nodes at any location. Experiments on three HSI datasets indicate that the MSLGCN can obtain better classification performance when compared with the other eleven state-of-the-art methods.