LAUSR

Hyperspectral image (HSI) change detection aims to identify the differences in multi-temporal HSIs. Recently, 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 convolutional neural network (CNN) that can only perform convolution operations on data with the assumption of Euclidean structure, GCN adopts a graph structure to flexibly capture the characteristics and structure information of non-Euclidean data. In this paper, we propose a novel dual-branch difference amplification GCN (D2AGCN) for HSI change detection with limited samples, which allows the network to fully extract and effectively amplify the difference features of multi-temporal 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 multi-temporal HSIs in the dual-branch structure, which increases the distinction between change and non-change classes. The visual and quantitative experimental results on three real hyperspectral datasets (i.e., China, Bay Area, and Santa Barbara) show that the proposed D2AGCN outperforms most of the state-of-the-art methods in HSI change detection with limited training samples.