A gradient-constrained morphological filtering algorithm for airborne LiDAR

Li, Yong; Wu, Huayi; Xu, Hua; An, Ru; Xu, Jun; He, Qisheng

doi:10.1016/j.optlastec.2013.06.007

Cited by 46 publications

(39 citation statements)

References 20 publications

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“…The Sohn filter had heavy bias towards type II errors, while the Pfeifer had heavy bias towards type I errors. The proposed filter in this paper produced more type II errors than the Axelsson filter but less than the filter in [38]. The produced type II errors were mainly at the bottom where laddered buildings occurred.…”

Section: Resultsmentioning

confidence: 70%

“…Although the filters proposed by Li et al [38] and this paper are both based on mathematical morphology, the strategies and performances of the two filters are different. Nonground objects usually appear around the feature points with dramatic height changes, so Li et al [38] focused on the redefinition of morphological openings to limit the filtered areas.…”

Section: Datamentioning

confidence: 84%

“…These characteristics would influence the subsequent gradient analysis. Hence, low outliers need to be removed beforehand using the following criteria [38]:…”

Section: Filter Implementationmentioning

confidence: 99%

“…Nonground objects usually appear around the feature points with dramatic height changes, so Li et al [38] focused on the redefinition of morphological openings to limit the filtered areas. The morphological erosion and dilation were only performed on the nearby points of the feature points, which could reduce the possibility of cutting the smooth terrain with a gradual height change.…”

Section: Datamentioning

confidence: 99%

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

Yong

et al. 2014

Remote Sensing

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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.

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Section: Resultsmentioning

confidence: 70%

Section: Datamentioning

confidence: 84%

“…These characteristics would influence the subsequent gradient analysis. Hence, low outliers need to be removed beforehand using the following criteria [38]:…”

Section: Filter Implementationmentioning

confidence: 99%

Section: Datamentioning

confidence: 99%

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

Yong

et al. 2014

Remote Sensing

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“…morphological filters for classification in general areas are performing well, accurate classification of lidar point clouds in complex urban scenes including large and small objects with diverse elevation [2][3][4] is challenging. Classification results often depend on the assumptions made to the neighbours of each lidar point [5].…”

Section: Introductionmentioning

confidence: 99%

Autocorrelation Statistics-Based Algorithms for Automatic Ground and Non-Ground Classification of Lidar Data

Shirowzhan

Lim

2014

Proceedings of the International Symposium on Automation and Robotics in Construction (IAARC)

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-Classification of lidar data to ground and nonground points is important for accurate topography mapping and reliable estimation of slope, volume and buildings' geometry over urban areas. Manual or semi-automatic classification provides relatively good results, however, automatic classification in complex areas with diverse object sizes is still challenging. This research aims to propose two novel algorithms based on Getis-Ord

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Extraction of digital terrain model based on regular mesh generation in mountainous areas

Han

et al. 2017

Multimed Tools Appl

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A gradient-constrained morphological filtering algorithm for airborne LiDAR

Cited by 46 publications

References 20 publications

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

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

Autocorrelation Statistics-Based Algorithms for Automatic Ground and Non-Ground Classification of Lidar Data

Extraction of digital terrain model based on regular mesh generation in mountainous areas

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