LiDAR Height Data Filtering using Empirical Mode Decomposition

Automatic extraction of bare-Earth LiDAR points to generate Digital Terrain Model (DTM) is still an ongoing problem. Even though there are several methods for ground filtering, automatic and adaptive methods are still a need due to the complexity of the environment. In this study, we address the ground filtering problem by applying Empirical Mode Decomposition (EMD) to the airborne LiDAR data. EMD is a data-driven method that adapts to the local characteristics of the signal. We benefit from EMD to extract the local trend of the LiDAR height data. This way, can extract a local adaptive threshold to filter ground and non-ground objects. We tested our method using the ISPRS LiDAR reference dataset and obtained promising results. We also compared the filtering results with the ones in the literature to show the improvements obtained.