In recent years, there have been many advances in sensor technology which provides more useful information about the observed scene. Two of the newest remote sensing technologies are hyperspectral (HS) and Light Detection And Ranging (LiDAR) sensors. Since pixels in a small spatial neighborhood are more likely to share similar abundances, hypergraph regularization (HG-NMF) can be employed to handle the similarity relevance among the spatial neighborhood pixels. In this paper, we provide a LiDAR data-aided HS unmixing using HG-NMF. The composite usage of all these valuable information can lead to higher accuracy unmixing results. The obtained convex optimization problem is solved by Spectral Unmixing by Split Augmented Lagrangian (SUnSAL-TV) Algorithm. Experiments on synthetic data are conducted. The advantage of HG-NMF regularization is also demonstrated.