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AWSF algorithm performance is compared against 14 filtering algorithms published between 1998 and 2019, as well as one unpublished (proprietary) algorithm designed by the world-leading company in processing remote sensing data, Harris Geospatial Solutions.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.
Highlights
H IGH-RESOLUTION digital terrain models (DTMs) can be generated from raw airborne light detection and ranging (LiDAR) data [1]
Both of the raw LiDAR data and reference DTMs were obtained through EDINA digimap LiDAR data service [52]
The accuracy of the automatic weighted splines filter (AWSF) algorithm is estimated by calculating the root-mean-square error (RMSE), minimum error, maximum error, and the mean error for each sample of the forested areas [3] as Another very important comparison has been made; the four forested areas were filtered using professional software designed by the world-leading company in processing remote sensing data, Harris Geospatial Solutions
Summary
H IGH-RESOLUTION digital terrain models (DTMs) can be generated from raw airborne light detection and ranging (LiDAR) data [1]. The interpolation-based methods work on predicting the terrain surface by using a linear function to interpolate the data and a weight value could be assigned to the measurements [27]. Cai et al [41] presented a model transfer-based filtering methodology that combines transfer learning theory with active learning for identifying the terrain points It requires a training dataset as a core for this method. Bayram et al [42] exploited weighted graph representations for the analysis of LiDAR data This method considered the data as irregularly distributed signals and applied an iterative graph signal filter to remove the off-terrain measurements from the raw data.
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