An Improved Top-Hat Filter with Sloped Brim for Extracting Ground Points from Airborne Lidar Point Clouds

Abstract

Airborne light detection and ranging (lidar) has become a powerful support for acquiring geospatial data in numerous geospatial applications and analyses. However, the process of extracting ground points accurately and effectively from raw point clouds remains a big challenge. This study presents an improved top-hat filter with a sloped brim to enhance the robustness of ground point extraction for complex objects and terrains. The top-hat transformation is executed and the elevation change intensity of the transitions between the obtained top-hats and outer brims is inspected to suppress the omission error caused by protruding terrain features. Finally, the nonground objects of complex structures, such as multilayer buildings, are identified by the brim filter that is extended outward. The performance of the proposed filter in various environments is evaluated using diverse datasets with difficult cases. The comparison of the proposed filter with the commercial software Terrasolid TerraScan and other popular filtering algorithms demonstrates the applicability and effectiveness of this filter. Experimental results show that the proposed filter has great promise in terms of its application in various types of landscapes. Abrupt terrain features with dramatic elevation changes are well preserved, and diverse objects with complicated shapes are effectively removed. This filter has minimal omission and OPEN ACCESSRemote Sens. 2014, 6 12886 commission error oscillation for different test areas and thus demonstrates a stable and reliable performance in diverse landscapes. In addition, the proposed algorithm has high computational efficiency because of its simple and efficient data structure and implementation.

Airborne LiDAR Data Filtering Based on Geodesic Transformations of Mathematical Morphology

“…The omission error in the middle is eliminated as shown in Figure 13b. Besides the above comparison between the proposed method and the classic morphology-based algorithm in Reference [44], Table 3 shows the three types of average errors of other algorithms performed against ISPRS datasets. Jahromi et al [46] employed the filter based on artificial neural networks.…”

“…To verify the feasibility and effectiveness of this algorithm, the results are compared with the classic morphology-based algorithms published in [44]. Li et al [44] employs the morphological top-hat transformation with progressively increased windows and the constraints along directions to reduce the removal of protruding terrain features when using large windows. Comparing with other popular approaches, Li et al [44] obtains the reliable and competitive performance for varied survey areas.…”

“…Li et al [44] employs the morphological top-hat transformation with progressively increased windows and the constraints along directions to reduce the removal of protruding terrain features when using large windows. Comparing with other popular approaches, Li et al [44] obtains the reliable and competitive performance for varied survey areas. Additionally, the comparison is also made with the filtering performance of the widely used commercial software Terrasolid TerraScan, which requires manual tuning of relevant parameters for different environments [40,45].…”

An Improved Morphological Algorithm for Filtering Airborne LiDAR Point Cloud Based on Multi-Level Kriging Interpolation

“…These algorithms can be classified into three categories namely, slope-based filtering algorithms [8][9][10][11], interpolation-based filtering algorithms [2,[12][13][14][15][16] and morphology-based filtering algorithms [17][18][19][20][21][22]. A slope-based filtering algorithm was first proposed by Vosselman [8].…”

GROUND FILTERING LiDAR DATA BASED ON MULTI-SCALE ANALYSIS OF HEIGHT DIFFERENCE THRESHOLD

“…Meanwhile, LiDAR has several advantages in comparison with traditional field surveying and photogrammetric mapping, e.g., cost-effective over a large area for acquisition of vertical information, higher accuracy and gathering information in any kinds of weather (Meng et al 2009;Li et al 2014). …”