Airborne LiDAR Point Cloud Filtering by a Multilevel Adaptive Filter Based on Morphological Reconstruction and Thin Plate Spline Interpolation

Meng, Xuesong; Lin, Yi; Yan, Lei; Gao, Xianlian; Yao, Yunjun; Wang, Cheng; Luo, Shuai

doi:10.3390/electronics8101153

Cited by 17 publications

(8 citation statements)

References 36 publications

(61 reference statements)

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“…In previous works, e.g., [19,28,[67][68][69], it was found that noise reduction during preprocessing yielded a better digital terrain model, but on the other hand this procedure could sometimes reduce the accuracy of the model, as [70] found in their study. We had a hypothesis that the noise reduction of the point cloud results in more accurate models in our case, and we pointed out that different noise reduction techniques can have a significant effect on the input data which are the basis of the next stage of the process, i.e., ground point classification.…”

Section: Discussionmentioning

confidence: 71%

“…Besides the fixed kernel windows, other robust methods exist, including the iterative multiscale spline, which was developed directly for densely forested areas [25]. Furthermore, [26] used a progressive morphology, [27] used a segmentation-based robust interpolation, and [28] used a combination of a multilevel adaptive filter (MAF) with morphological reconstruction and a thin plate spline (TPS) interpolation algorithm for the classification procedure to extract more precisely the bare earth. In addition, nowadays, there is an increasing number of easy-to-use algorithms which are implemented in freely available software to help for the users with the filtering process.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Aerial Laser Scanning Data as a Source of Terrain Modeling in a Fluvial Environment: Biasing Factors of Terrain Height Accuracy

Szabó

Tóth

Holb

et al. 2020

Sensors

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Airborne light detection and ranging (LiDAR) scanning is a commonly used technology for representing the topographic terrain. As LiDAR point clouds include all surface features present in the terrain, one of the key elements for generating a digital terrain model (DTM) is the separation of the ground points. In this study, we intended to reveal the efficiency of different denoising approaches and an easy-to-use ground point classification technique in a floodplain with fluvial forms. We analyzed a point cloud from the perspective of the efficiency of noise reduction, parametrizing a ground point classifier (cloth simulation filter, CSF), interpolation methods and resolutions. Noise filtering resulted a wide range of point numbers in the models, and the number of points had moderate correlation with the mean accuracies (r = −0.65, p < 0.05), indicating that greater numbers of points had larger errors. The smallest differences belonged to the neighborhood-based noise filtering and the larger cloth size (5) and the smaller threshold value (0.2). The most accurate model was generated with the natural neighbor interpolation with the cloth size of 5 and the threshold of 0.2. These results can serve as a guide for researchers using point clouds when considering the steps of data preparation, classification, or interpolation in a flat terrain.

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Section: Discussionmentioning

confidence: 71%

Section: Introductionmentioning

confidence: 99%

Aerial Laser Scanning Data as a Source of Terrain Modeling in a Fluvial Environment: Biasing Factors of Terrain Height Accuracy

Szabó

Tóth

Holb

et al. 2020

Sensors

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“…In recent decades, many ground filtering methods have been proposed to separate ground points from point clouds. According to the filtering strategies used, these methods can be grouped into five primary categories: interpolation-based [13][14][15][16][17][18][19][20][21][22][23][24][25][26], slope-based [27][28][29][30][31][32][33], morphological-based [34][35][36][37][38][39][40], segmentation-based [41][42][43][44][45][46][47][48][49], and simulation-based [50][51][52][53] approaches. Each category has shown its advantages in dealing with different kinds of topography.…”

Section: Introductionmentioning

confidence: 99%

An Adaptive Surface Interpolation Filter Using Cloth Simulation and Relief Amplitude for Airborne Laser Scanning Data

Zhu

Luo

et al. 2021

Remote Sensing

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Separating point clouds into ground and nonground points is an essential step in the processing of airborne laser scanning (ALS) data for various applications. Interpolation-based filtering algorithms have been commonly used for filtering ALS point cloud data. However, most conventional interpolation-based algorithms have exhibited a drawback in terms of retaining abrupt terrain characteristics, resulting in poor algorithmic precision in these regions. To overcome this drawback, this paper proposes an improved adaptive surface interpolation filter with a multilevel hierarchy by using a cloth simulation and relief amplitude. This method uses three hierarchy levels of provisional digital elevation model (DEM) raster surfaces with thin plate spline (TPS) interpolation to separate ground points from unclassified points based on adaptive residual thresholds. A cloth simulation algorithm is adopted to generate sufficient effective initial ground seeds for constructing topographic surfaces with high quality. Residual thresholds are adaptively constructed by the relief amplitude of the examined area to capture complex landscape characteristics during the classification process. Fifteen samples from the International Society for Photogrammetry and Remote Sensing (ISPRS) commission are used to assess the performance of the proposed algorithm. The experimental results indicate that the proposed method can produce satisfying results in both flat areas and steep areas. In a comparison with other approaches, this method demonstrates its superior performance in terms of filtering results with the lowest omission error rate; in particular, the proposed approach retains discontinuous terrain features with steep slopes and terraces.

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“…The capturing distance of Airborne Laser Scanning (ALS) is relatively consistent and the sampling density is much sparser. Considering limitation of scanning degree and flight elevation, ALS point clouds are commonly processed as 2.5D data for filtering and typical methods mainly include mathematical morphology (Li et al, 2013;Li et al, 2017;Meng et al, 2019), surface interpolation (Hu et al, 2014), segmentation (Chen et al, 2016), etc., which are discussed in this paper.…”

Section: Introductionmentioning

confidence: 99%

“…Curved surface-based methods perform better to preserve sharp details and conform terrain relief. Least-square, Thin-Plate-Spline (TPS) (Meng et al, 2019), and scanline spline (Martí nez Sá nchez et al, 2019) are commonly used to fit the surface while accurate interpolation points are strictly required for reasonable fitting. Improvements involve seed point selection such as moving-window weighted iterative least-squares fitting (Qin et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Terrain-Adaptive Ground Filtering of Airborne Lidar Data Based on Saliency-Aware Thin Plate Spline

Liu

Zhang

Huang

et al. 2020

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. Ground filtering separates the ground and non-ground points from point clouds, which is the essential process for DEM generation, semantic segmentation, model reconstruction and so forth. Considering the topologically complex terrain environments, the segmentation results are prone to be disturbed dealing with steep slopes, buildings, bridges, cliffs, etc. from Airborne LiDAR point clouds. In this paper, a saliency-aware Thin-Plate-Spline (SATPS) interpolation method is proposed including two steps: saliency division and adaptive regularized TPS interpolation with relative variance coefficient. Firstly, the point clouds are indexed in 2D grids and segments are clustered step probing toward 8-adjacent scanning directions. Then, the saliency of each grid is calculated according to the elevation variance of adjacent segments towards each scanning direction. Subsequently, grids of high ground saliency are considered as candidates for seed point selection and then clustered by region growing. The TPS surface is interpolated for each cluster loosely fitting to the seed points involving an adaptive relative variance coefficient which is according to ground saliency and elevation deviation. And finally, the ground points are extracted around the TPS surface. Experimental results indicate that the proposed SATPS algorithm achieves better Type 1 accuracy and total accuracy than the state-of-the-art algorithms in scenes with complex terrain structures, which is practical to generate DEM products.

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Airborne LiDAR Point Cloud Filtering by a Multilevel Adaptive Filter Based on Morphological Reconstruction and Thin Plate Spline Interpolation

Cited by 17 publications

References 36 publications

Aerial Laser Scanning Data as a Source of Terrain Modeling in a Fluvial Environment: Biasing Factors of Terrain Height Accuracy

Aerial Laser Scanning Data as a Source of Terrain Modeling in a Fluvial Environment: Biasing Factors of Terrain Height Accuracy

An Adaptive Surface Interpolation Filter Using Cloth Simulation and Relief Amplitude for Airborne Laser Scanning Data

Terrain-Adaptive Ground Filtering of Airborne Lidar Data Based on Saliency-Aware Thin Plate Spline

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