Dual Graph Convolution Joint Dense Networks for Hyperspectral and LiDAR Data Classification

With the increasing demand of observation, multi-source remote sensing data has been widely used. Hyperspectral Images (HSI) and Light Detection and Ranging (LiDAR) data have shown the great potential in land cover classification. However, the redundant information of multi-source data influences the effectiveness of heterogeneous data features, which reduces the accuracy of joint classification. To tackle this problem, a dual graph convolution joint dense networks is proposed for HSI and LiDAR classification. In this method, a dual graph convolution network (GCN)is extracted the spectral feature from euclidean graph and cosine graph, which contains the spectrum absolute and relative differences. A dense network is employed to acquire spatial feature from LiDAR data. Finally, a fully connected network fuses the spectral and spatial feature for classification. Experiments conducted on the Huston dataset demonstrate the effectiveness of the proposed method on joint classification.