Dual-Branch Difference Amplification Graph Convolutional Network for Hyperspectral Image Change Detection

Abstract

Hyperspectral image (HSI) change detection aims to identify the differences in multitemporal HSIs. Recently, a graph convolutional network (GCN) has attracted increasing attention in the field of remote sensing due to its advantages in processing irregular data. In comparison with a convolutional neural network (CNN) that can only perform convolution operations on data with the assumption of the Euclidean structure, GCN adopts a graph structure to flexibly capture the characteristics and structure information of non-Euclidean data. In this article, we propose a novel dual-branch difference amplification GCN (D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> AGCN) for HSI change detection with limited samples, which allows the network to fully extract and effectively amplify the difference features of multitemporal HSIs for change detection. The dual-branch structure can effectively extract sufficient different features to facilitate the detection of the changed areas. As far as we know, this is the first time that GCN has been introduced into HSI change detection. A difference magnification module is designed to suppress similar regions and highlight the feature differences between the multitemporal HSIs in the dual-branch structure, which increases the distinction between change and nonchange classes. The visual and quantitative experimental results on three real hyperspectral datasets (i.e., China, Bay Area, and Santa Barbara) show that the proposed D <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> AGCN outperforms most of the state-of-the-art methods in HSI change detection with limited training samples.