An improved multi-resolution hierarchical classification method based on robust segmentation for filtering ALS point clouds

Chen, Chuanfa; Li, Yanyan; Yan, Changqing; Dai, Hang; Liu, Guolin; Guo, Jinyun

doi:10.1080/01431161.2016.1142687

Cited by 32 publications

(19 citation statements)

References 40 publications

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“…However, these slope-based filters could not obtain a good result in the steep-slope areas because of using a single fixed slope threshold [35]. In the mixed-slope area, where flat and hilly areas are interspersed, using a fixed threshold value can miss much of the ground information [35].…”

Section: Slope-based Seed Points Extractionmentioning

confidence: 99%

“…Surface interpolation-based filters [39][40][41] assume that the terrain can be approximated by parametric surfaces. Previous studies [30,35] ascertained that surface interpolation-based filters performed better than the others, but with a limitation in steep-slope landscapes. It has been a challenging and an intriguing task for researchers to develop a filtering algorithm that works precisely for steep mountainous terrain [24,27,29].…”

Section: Introductionmentioning

confidence: 99%

“…Current filtering algorithms developed for ALS can be divided into three main categories; slope-based, morphology-based, and surface interpolation-based approaches [22,[32][33][34][35]. The slope-based filters [28,36] assume that the slope between terrain points is gradual.…”

Section: Introductionmentioning

confidence: 99%

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A New Recursive Filtering Method of Terrestrial Laser Scanning Data to Preserve Ground Surface Information in Steep-Slope Areas

Kim

Sohn

et al. 2017

IJGI

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Landslides are one of the critical natural hazards that cause human, infrastructure, and economic losses. Risk of catastrophic losses due to landslides is significant given sprawled urban development near steep slopes and the increasing proximity of large populations to hilly areas. For reducing these losses, a high-resolution digital terrain model (DTM) is an essential piece of data for a qualitative or a quantitative investigation of slopes that may lead to landslides. Data acquired by a terrestrial laser scanning (TLS), called a point cloud, has been widely used to generate a DTM, since a TLS is appropriate for detecting small-to large-scale ground features on steep slopes. For an accurate DTM, TLS data should be filtered to remove non-ground points, but most current algorithms for extracting ground points from a point cloud have been developed for airborne laser scanning (ALS) data and not TLS data. Moreover, it is a challenging task to generate an accurate DTM from a steep-slope area by using existing algorithms. For these reasons, we developed an algorithm to automatically extract only ground points from the point clouds of steep terrains. Our methodology is focused on TLS datasets and utilizes the adaptive principal component analysis-triangular irregular network (PCA-TIN) approach. Our method was applied to two test areas and the results showed that the algorithm can cope well with steep slopes, giving an accurate surface model compared to conventional algorithms. Total accuracy values of the generated DTMs in the form of root mean squared errors are 1.84 cm and 2.13 cm over the areas of 5252 m 2 and 1378 m 2 , respectively. The slope-based adaptive PCA-TIN method demonstrates great potential for TLS-derived DTM construction in steep-slope landscapes.

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Section: Slope-based Seed Points Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Recursive Filtering Method of Terrestrial Laser Scanning Data to Preserve Ground Surface Information in Steep-Slope Areas

Kim

Sohn

et al. 2017

IJGI

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“…Moreover, ALS point clouds have other benefits such as no effects of relief displacement, penetration of vegetation, and insensitivity to lighting conditions [1]. Therefore, ALS technique has been effectively used for ground point detection [3][4][5][6][7], topographic mapping [8], 3D city modelling [9][10][11][12][13], object recognition [14][15][16], solar energy estimation [17], etc.…”

Section: Introductionmentioning

confidence: 99%

Classification of ALS Point Cloud with Improved Point Cloud Segmentation and Random Forests

Lin

Zhang

2017

Remote Sensing

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This paper presents an automated and effective framework for classifying airborne laser scanning (ALS) point clouds. The framework is composed of four stages: (i) step-wise point cloud segmentation, (ii) feature extraction, (iii) Random Forests (RF) based feature selection and classification, and (iv) post-processing. First, a step-wise point cloud segmentation method is proposed to extract three kinds of segments, including planar, smooth and rough surfaces. Second, a segment, rather than an individual point, is taken as the basic processing unit to extract features. Third, RF is employed to select features and classify these segments. Finally, semantic rules are employed to optimize the classification result. Three datasets provided by Open Topography are utilized to test the proposed method. Experiments show that our method achieves a superior classification result with an overall classification accuracy larger than 91.17%, and kappa coefficient larger than 83.79%.

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“…Airborne Laser Scanning (ALS) point clouds have many benefits in contrast with the commonly used 2D remote sensing images for a variety of applications, such as ground point extraction (Sithole and Vosselman, 2004;Meng et al, 2010;Chen et al, 2016;Zhang and Lin, 2013;Yang et al, 2016), 3D city modelling (Sampath and Shan, 2007;Chen et al, 2014;Jarzgbek-Rychard and Borkowski, 2016;Sampath and Shan, 2010;Yu et al, 2016), etc. Over the past two decades, significant contributions to the consolidation and extension of ALS data processing methods have been witnessed (Yan et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Multiple-Primitives-Based Hierarchical Classification of Airborne Laser Scanning Data in Urban Areas

Lin

Zhang

2017

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

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ABSTRACT:A hierarchical classification method for Airborne Laser Scanning (ALS) data of urban areas is proposed in this paper. This method is composed of three stages among which three types of primitives are utilized, i.e., smooth surface, rough surface, and individual point. In the first stage, the input ALS data is divided into smooth surfaces and rough surfaces by employing a step-wise point cloud segmentation method. In the second stage, classification based on smooth surfaces and rough surfaces is performed. Points in the smooth surfaces are first classified into ground and buildings based on semantic rules. Next, features of rough surfaces are extracted. Then, points in rough surfaces are classified into vegetation and vehicles based on the derived features and Random Forests (RF). In the third stage, point-based features are extracted for the ground points, and then, an individual point classification procedure is performed to classify the ground points into bare land, artificial ground and greenbelt. Moreover, the shortages of the existing studies are analyzed, and experiments show that the proposed method overcomes these shortages and handles more types of objects.

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An improved multi-resolution hierarchical classification method based on robust segmentation for filtering ALS point clouds

Cited by 32 publications

References 40 publications

A New Recursive Filtering Method of Terrestrial Laser Scanning Data to Preserve Ground Surface Information in Steep-Slope Areas

A New Recursive Filtering Method of Terrestrial Laser Scanning Data to Preserve Ground Surface Information in Steep-Slope Areas

Classification of ALS Point Cloud with Improved Point Cloud Segmentation and Random Forests

Multiple-Primitives-Based Hierarchical Classification of Airborne Laser Scanning Data in Urban Areas

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