A progressive morphological filter for removing nonground measurements from airborne LIDAR data

Abstract

Abstract-Recent advances in airborne light detection and ranging (LIDAR) technology allow rapid and inexpensive measurements of topography over large areas. This technology is becoming a primary method for generating high-resolution digital terrain models (DTMs) that are essential to numerous applications such as flood modeling and landslide prediction. Airborne LIDAR systems usually return a three-dimensional cloud of point measurements from reflective objects scanned by the laser beneath the flight path. In order to generate a DTM, measurements from nonground features such as buildings, vehicles, and vegetation have to be classified and removed. In this paper, a progressive morphological filter was developed to detect nonground LIDAR measurements. By gradually increasing the window size of the filter and using elevation difference thresholds, the measurements of vehicles, vegetation, and buildings are removed, while ground data are preserved. Datasets from mountainous and flat urbanized areas were selected to test the progressive morphological filter. The results show that the filter can remove most of the nonground points effectively.Index Terms-Airborne laser altimetry, digital terrain model (DTM), light detection and ranging (LIDAR) data filtering.

Performance Evaluation of Different Ground Filtering Algorithms for Uav-Based Point Clouds

“…These operations are used to increase or decrease the size of the objects (Haralick et al, 1987). Combinations of the erosion and dilation operations leads to opening and closing operations (Zhang et al, 2003). Dilation follows erosion in opening operation; whereas erosion follows dilation in closing operation.…”

“…Otherwise, the point is labelled as a non-ground point. The iterations continue until the size of the window is greater than a predefined maximum value, which is generally slightly larger than the size of the largest non-ground object (Zhang et al, 2003). The only difference between the PM1D and PM2D algorithms is that the PM2D algorithm uses a two-dimensional square window to perform erosion and dilation.…”

“…It may not always be practical to investigate the performances of ground filtering algorithms by using all points in data. Hence, Zhang et al, (2003), and Zhang and Whitman (2005) proposed to evaluate the filtering result by using randomly selected test points. In this approach, some random points are selected.…”

Performance Evaluation of Different Ground Filtering Algorithms for Uav-Based Point Clouds

“…The PM algorithm was developed by Zhang et al, (2003) to remove the non-ground points from the point cloud. This algorithm removes the different-sized non-ground objects and keeps the ground points by using gradually increasing window and elevation difference threshold.…”

“…All test points are then used in quantitative evaluation. The approach proposed by Zhang et al, (2003) and Zhang and Whitman (2005) was used to evaluate the ground filtering results. Hence, 100 random points were selected from each point cloud (i.e.…”