A voxel-based multiscale morphological airborne lidar filtering algorithm for digital elevation models for forest regions

Liu, Li; Lim, Samsung

doi:10.1016/j.measurement.2018.03.020

Cited by 19 publications

(8 citation statements)

References 20 publications

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“…In accordance with the presented study, it can be concluded that the algorithms of existing software solutions, such as Terrasolid, Global mapper, MATLAB, and others do not always guarantee high quality results, and often do not have adequate universality for all types of territories. Moreover, research groups from other countries [11][12][13] faced the same problem. In their scientific works, they offer their own methods of decoding laser points of reflections [11][12][13][14].…”

Section: Resultsmentioning

confidence: 99%

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Classification and Automatisation of Laser Reflection Points Processing in the Detection of Vegetation

Gura¹,

Kuziakina²,

Dubenko³

et al. 2019

Proceedings of the International Symposium "Engineering and Earth Sciences: Applied and Fundamental Research" Dedicated to the

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The article touches upon the issue of automatic recognition and subsequent processing of data on natural and industrial objects of the world. The obtained data is a result of laser scanning. For data analysis in 2018 a research group, consisting of specialists from Kuban State Technological University, Kuban State University, Kuban State Agrarian University and Aerogeomatics Company, conducted a joint research on the decoding of forest cover based on the data of airborne laser scanning under various landscape conditions. The analysis of existing software was performed through the comparison of various methods of automated decoding and the subsequent decoding of points on the basis of airborne laser scanning. Various research results on this topic, including foreign studies, are analyzed. The authors made the conclusions about the quality and reliability of the information provided by each of the products and the level of development of this software segment as a whole. The alternative development options for this industry based on the use of neural networks are given. Keywordslaser scanning; classification of cloud of laser points; neural networks. I.

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Section: Resultsmentioning

confidence: 99%

“…Moreover, research groups from other countries [11][12][13] faced the same problem. In their scientific works, they offer their own methods of decoding laser points of reflections [11][12][13][14].…”

Section: Resultsmentioning

confidence: 99%

Classification and Automatisation of Laser Reflection Points Processing in the Detection of Vegetation

Gura¹,

Kuziakina²,

Dubenko³

et al. 2019

Proceedings of the International Symposium "Engineering and Earth Sciences: Applied and Fundamental Research" Dedicated to the

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“…Generally, these filters achieve a good performance in relatively flat terrains with simple landscapes, but show poor results in mountainous terrains, cliffs, ridges and discontinuities with complex environments [7]. Compared to the other filtering methods, the interpolation-based filters commonly obtain better results since they can use more contexts [5]; nevertheless, they cannot perform well in dense forested areas, because the canopy prevents most laser pulses from reaching the ground surface, resulting in few ground points beneath the canopy [8]. Although some specific methods [9]- [12] have been designed for forested areas, their performances are still unsatisfactory and require to be improved [13].…”

Section: Introductionmentioning

confidence: 99%

Multi-Level Interpolation-Based Filter for Airborne LiDAR Point Clouds in Forested Areas

et al. 2020

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Over the past decades, plenty of filtering algorithms have been presented to distinguish ground and non-ground points from airborne LiDAR point clouds. However, with the existing methods, it is difficult to derive satisfactory filtering results on rugged terrains with dense vegetation due to the low-level penetration ability of laser pulses. Therefore, a multi-level interpolation-based filter is developed in this paper. The novelty of the algorithm lies within its usage of multi-scale morphological operations and robust z-score to correctly select ground seeds as more as possible, and a terrain-adaptive residual threshold to adapt to various terrain characteristics. Rural samples provided by International Society for Photogrammetry and Remote Sensing (ISPRS) were employed to assess the performance of the proposed method and its results were compared with 15 filtering algorithms developed in recent 5 years (2015-2019). Results show that the proposed method with the optimized parameters produces the best accuracy with the average total error and kappa coefficient of 1.89% and 87.88%, respectively. We further filtered high-density point clouds in six forested areas with different vegetation covers and terrain slopes. Results demonstrate that the proposed algorithm is more accurate than the well-known filtering methods including morphological-based filter, progressive TIN densification filter (PTD), improved PTD and cloth simulation filter, with the average total error decreased by 26.2%, 19.9%, 3.8% and 40.4%, respectively. Moreover, the DEMs of the proposed method have lower average root mean square errors than the four classical filters. Therefore, the proposed method can be considered as an effective ground filtering algorithm for airborne LiDAR point clouds in forested areas. INDEX TERMS Filtering, point cloud, interpolation, digital elevation model.

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“…This process is repeated using different horizontal distance values that should start from a small value and increase gradually through the iterative steps [21], [22]. Liu and Lim [23] presented a voxel-based multiscale morphological filtering method that applies a convexity constraint to LiDAR measurements. Then, the morphological operations are applied with a moving window to classify the data into terrain and off-terrain points after applying Otsu segmentation with a threshold value as a last step.…”

Section: Introductionmentioning

confidence: 99%

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

Abdeldayem

2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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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.

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A voxel-based multiscale morphological airborne lidar filtering algorithm for digital elevation models for forest regions

Cited by 19 publications

References 20 publications

Classification and Automatisation of Laser Reflection Points Processing in the Detection of Vegetation

Classification and Automatisation of Laser Reflection Points Processing in the Detection of Vegetation

Multi-Level Interpolation-Based Filter for Airborne LiDAR Point Clouds in Forested Areas

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

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