Progressive Filtering of Airborne LiDAR Point Clouds Using Graph Cuts

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. The DTM is then extracted through progressive filtering via the graph cuts. The performance of the proposed method is investigated using two datasets with different point densities, terrain complexity, and land covers. The results show that the filter can effectively remove nonterrain points, leading to an accurately extracted DTM. The filter is also compared with other methods reported in the literature, the comparison demonstrating that the proposed method exhibits advantages in terms of performance.