Using empirical mode decomposition for ground filtering

LiDAR data provides valuable information for various remote sensing applications. For these, one important and challenging problem is ground filtering. This operation separates the bare earth and object data. Researchers proposed several methods to solve this problem. However, the complexity of the data limit the usability of these methods for all terrain types. Besides, the performance obtained in ground filtering should be improved further. In this study, we focus on this problem and propose a novel ground filtering method using Empirical Mode Decomposition (EMD). We tested the proposed method on the standard ISPRS data set and evaluate its strengths and weaknesses. We also compared the proposed method with the ones in the literature to show the improvements obtained.