A Multihop Graph Rectify Attention and Spectral Overlap Grouping Convolutional Fusion Network for Hyperspectral Image Classification

Convolutional neural networks (CNNs) have been widely used in hyperspectral image (HSI) classification due to their ability to extract image features effectively. However, under the condition of limited samples, the modeling ability of CNNs for the relationships among samples is limited. At present, research on the classification of HSIs with a small number of samples remains an important challenge in the field of HSI processing. Recently, graph convolutional networks (GCNs) have been applied in HSI classification tasks. In this article, a multihop graph rectifies attention and spectral overlap grouping convolutional fusion network (MRSGFN) for HSI classification is proposed. In the graph convolution branch, a multihop graph rectify attention (MHRA) is designed to weight and correct the features extracted by graph convolution. In the convolutional branch, to solve the problem of dimensionality disaster caused by high spectral dimension with a small number of samples, a spectral intra group inter group feature extraction module (SI2FEM) based on spectral overlap grouping is constructed. In order to better fuse the features extracted from CNNs and GCNs, a Gaussian weighted fusion module (GWFM) is elaborately designed in this article. The features extracted by different branches are assigned different weights by GWFM through a 2-D Gaussian map and then fused. Numerous experiments were conducted on three common datasets and showed that the classification performance of the proposed MRSGFN is superior to other advanced methods.