  As the ground objects become increasingly complex, the classification results
obtained by single source remote sensing data can hardly meet the application
requirements. In order to tackle this limitation, we propose a simple yet
effective attention fusion model based on Disentangled Non-local (DNL) network
for hyperspectral and LiDAR data joint classification task. In this model,
according to the spectral and spatial characteristics of HSI and LiDAR, a
multiscale module and a convolutional neural network (CNN) are used to capture
the spectral and spatial characteristics respectively. In addition, the
extracted HSI and LiDAR features are fused through some operations to obtain
the feature information more in line with the real situation. Finally, the
above three data are fed into different branches of the DNL module,
respectively. Extensive experiments on Houston dataset show that the proposed
network is superior and more effective compared to several of the most advanced
baselines in HSI and LiDAR joint classification missions.
