Classfication of Hyperspectral Image With Attention Mechanism-Based Dual-Path Convolutional Network

Abstract

Recently, the convolutional neural network (CNN) has made great progress in hyperspectral image (HSI) classification because of its powerful feature extraction capability. However, the standard CNN based on grid sampling neglects the inherent relation between HSI data, which leads to poor regional edge delineation and generalization ability. Graph convolutional network (GCN) has been successfully applied to data representation in a non-Euclidean space, and it can extract discriminative embedded features by dynamically updating irregular graphs. In this letter, we propose a novel method termed attention mechanism-based dual-path convolutional network (AMDPCN), which is composed of a GCN-based global information learning model (GILM) and a CNN-based local feature extraction network (LFEN). Specifically, AMDPCN fuses the global spatial relationships explored by GILM and the local discriminant features extracted by LFEN with three different strategies: addition, multiplication, and concatenation. Furthermore, a multi-scale attention mechanism (MS-AM) is developed to mitigate the Hughes phenomenon by adaptive recalibrating the nonlinear interdependence among the features. Experiments on Kennedy Space Center and Indian Pines data sets demonstrate the advantages of the proposed AMDPCN to state-of-the-art methods.