Automatic Weighted Splines Filter (AWSF): A New Algorithm for Extracting Terrain Measurements From Raw LiDAR Point Clouds

Abstract

Airborne raw light detection and ranging (LiDAR) measurements are georeferenced three-dimensional coordinates of ground surface, including all natural and man-made features. Extracting terrain surface measurements from raw LiDAR data is referred to as "filtering." Many filtering algorithms have been published, indicating the difficulty of the task; however, none performs equally well on all kinds of landscapes. This article presents a new algorithm, automatic weighted splines filter (AWSF), to extract the terrain points from raw LiDAR measurements. The mathematical model of the AWSF algorithm utilizes both the cubic smoothing splines (to interpolate and fit raw LiDAR data) and z-shaped function (to estimate the weight value of each point). The AWSF algorithm performance is compared against 14 filtering algorithms published between 1998 and 2019, as well as one unpublished (proprietary) algorithm designed by the world-leading company in processing remote sensing data, Harris Geospatial Solutions. Diverse landscape scenarios are used, ranging from open fields, rural land, and urban areas to dense forests on mountains where most of the filtering algorithms struggle as these areas represent the most difficult and challenging landscapes because the canopy prevents LiDAR pulses from reaching the ground surface. A total of 19 samples were tested; the results clearly show that the filtered terrain measurements are accurate and that the performance of the AWSF algorithm is stable for all the LiDAR data samples in comparison with the other filtering approaches.

Region-Based Edge Convolutions With Geometric Attributes for the Semantic Segmentation of Large-Scale 3-D Point Clouds

“…Although handling such data is not as intuitive as processing images, point clouds are used in numerous applications. Examples include the generation of canopy models [1], individual tree segmentation [2], building models, digital terrain elevation models [3], or semantic understanding of forest and urban areas. The latter can be achieved via semantic segmentation.…”