DEM Retrieval From Airborne LiDAR Point Clouds in Mountain Areas via Deep Neural Networks

Airborne light detection and ranging (LiDAR) remote sensing enables accurate estimation and monitoring of terrain and vegetation, and digital surface model (DSM) and digital elevation model (DEM) are vital analytical tools to achieve this estimation and monitoring. Among them, DSM can be directly acquired from airborne LiDAR point clouds; nevertheless, for the production of DEM, point clouds representing a surface of ground objects should be accurately filtered out at first. In some mountain forest areas, due to the limited penetration of airborne LiDAR, ground points sustain a serious lack, which results in the difficulty in producing accurate DEMs. To reduce the intricacy and subjectivity caused by the manual supplement to ground points, this letter proposes a new DEM retrieval method from airborne LiDAR point clouds in mountain areas based on deep neural networks (DNNs). With a DNN model trained by accurate DEMs and DSMs, DEM retrieval becomes much easier by inputting their DSM into this model for prediction. Experiments on Fujian and Hainan mountain data sets demonstrate the effectiveness of this supervised method.