Progressive Filtering of Airborne LiDAR Point Clouds Using Graph Cuts

Abstract

The development of robust and accurate filtering approaches for automated extraction of digital terrain models (DTMs) from airborne Light Detection and Ranging (LiDAR) data continues to be a challenge. The problem is due to the nature of LiDAR point clouds, the complexity of scene components, and the intrinsic structure of the terrain itself. This paper proposes a novel approach for filtering LiDAR point clouds, which exploits the spatial structure of the terrain and the spatial coherence among the LiDAR points. Terrain points are progressively detected through energy minimization using graph cuts. The energy function and graph model encode both pointwise closeness and pairwise smoothness. T..