Spectral-Spatial Graph Convolutional Network for Hyperspectral and SAR Data Fusion

Bin Deng, Puhong Duan, Xukun Lu, Wang Liu, Xudong Kang

Abstract: Hyperspectral image (HSI) provides rich spatial and spectral information of ground objects, while synthetic aperture radar (SAR) records scattering information such as shape and structure. Fusion of HSI and SAR can improve the classification performance of land covers. In recent years, graph convolutional networks (GCN) have been widely used in the field of remote sensing due to its advantages in processing non-Euclidean structures, capturing local and global information. In this paper, we propose a spectral-spatial graph convolutional network (SSGCN) for fusion of HSI and SAR. First, the GCN is utilized to extract the spatial information of HSI and SAR. Then, a convolutional neural network is applied to extract the spectral information of HSI. Finally, the extracted spectral and spatial features are merged together followed by a fully connected layer to obtain the final classification result. Experiments on two datasets, i.e., Berlin and Augsburg, reveal that the proposed SSGCN significantly outperforms other representative methods.