Automatic Segmentation of Raw LIDAR Data for Extraction of Building Roofs

Awrangjeb, Mohammad; Fraser, Clive S.

doi:10.3390/rs6053716

Cited by 127 publications

(135 citation statements)

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“…For the D s ij between Vi and Vj in this 4 dimensional feature space is calculated using the histogram intersection kernel (Papon et al, 2013). For the connectivity, it corresponds to the smoothness (Awrangjeb and Fraser, 2014) and convexity criterion (Stein et al, 2014) formed by the points surfaces of adjacent voxels. In Fig.…”

Section: Geometric Cues Using Perceptual Lawsmentioning

confidence: 99%

“…The quantitative evaluation is conducted by comparing the segments against the manually segmented ground truth (see Fig. 6) using the approach described in Awrangjeb and Fraser (2014) and Vo et al (2015). Three standard metrics, P recision, Recall, and F1 -score, which are calculated via the true positive (TP), the true negative (TN), the false negative (FN), and false positive (FP), are introduced to assess the quality of segmentation.…”

Section: Experimental Datasetsmentioning

confidence: 99%

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Voxel- And Graph-Based Point Cloud Segmentation of 3d Scenes Using Perceptual Grouping Laws

Hoegner

Tuttas

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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ABSTRACT:Segmentation is the fundamental step for recognizing and extracting objects from point clouds of 3D scene. In this paper, we present a strategy for point cloud segmentation using voxel structure and graph-based clustering with perceptual grouping laws, which allows a learning-free and completely automatic but parametric solution for segmenting 3D point cloud. To speak precisely, two segmentation methods utilizing voxel and supervoxel structures are reported and tested. The voxel-based data structure can increase efficiency and robustness of the segmentation process, suppressing the negative effect of noise, outliers, and uneven points densities. The clustering of voxels and supervoxel is carried out using graph theory on the basis of the local contextual information, which commonly conducted utilizing merely pairwise information in conventional clustering algorithms. By the use of perceptual laws, our method conducts the segmentation in a pure geometric way avoiding the use of RGB color and intensity information, so that it can be applied to more general applications. Experiments using different datasets have demonstrated that our proposed methods can achieve good results, especially for complex scenes and nonplanar surfaces of objects. Quantitative comparisons between our methods and other representative segmentation methods also confirms the effectiveness and efficiency of our proposals.

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Section: Geometric Cues Using Perceptual Lawsmentioning

confidence: 99%

Section: Experimental Datasetsmentioning

confidence: 99%

Voxel- And Graph-Based Point Cloud Segmentation of 3d Scenes Using Perceptual Grouping Laws

Hoegner

Tuttas

et al. 2017

ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci.

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show abstract

“…A height threshold is computed for each LiDAR point as Ht = Hg + H rf , where Hg is ground height and H rf is a relief factor that separates low objects from higher objects. For our study, we choose 1m relief factor in order to keep low height objects (Awrangjeb and Fraser, 2014b).…”

Section: Data Preprocessing and Line Segmentationmentioning

confidence: 99%

Fusion of Lidar Data and Multispectral Imagery for Effective Building Detection Based on Graph and Connected Component Analysis

Gilani

Awrangjeb

2015

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

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ABSTRACT:Building detection in complex scenes is a non-trivial exercise due to building shape variability, irregular terrain, shadows, and occlusion by highly dense vegetation. In this research, we present a graph based algorithm, which combines multispectral imagery and airborne LiDAR information to completely delineate the building boundaries in urban and densely vegetated area. In the first phase, LiDAR data is divided into two groups: ground and non-ground data, using ground height from a bare-earth DEM. A mask, known as the primary building mask, is generated from the non-ground LiDAR points where the black region represents the elevated area (buildings and trees), while the white region describes the ground (earth). The second phase begins with the process of Connected Component Analysis (CCA) where the number of objects present in the test scene are identified followed by initial boundary detection and labelling. Additionally, a graph from the connected components is generated, where each black pixel corresponds to a node. An edge of a unit distance is defined between a black pixel and a neighbouring black pixel, if any. An edge does not exist from a black pixel to a neighbouring white pixel, if any. This phenomenon produces a disconnected components graph, where each component represents a prospective building or a dense vegetation (a contiguous block of black pixels from the primary mask). In the third phase, a clustering process clusters the segmented lines, extracted from multispectral imagery, around the graph components, if possible. In the fourth step, NDVI, image entropy, and LiDAR data are utilised to discriminate between vegetation, buildings, and isolated building's occluded parts. Finally, the initially extracted building boundary is extended pixel-wise using NDVI, entropy, and LiDAR data to completely delineate the building and to maximise the boundary reach towards building edges. The proposed technique is evaluated using two Australian data sets: Aitkenvale and Hervey Bay, for object-based and pixel-based completeness, correctness, and quality. The proposed technique detects buildings larger than 50 m 2 and 10 m 2 in the Aitkenvale site with 100% and 91% accuracy, respectively, while in the Hervey Bay site it performs better with 100% accuracy for buildings larger than 10 m 2 in area.

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“…[13][14][15][16][17]; evaluation of different sets of characteristics calculated from a point and its neighborhood [18][19][20]; or multiclass classification techniques based on supervised machine learning algorithms [21][22][23]. In this way, it is possible to locate different elements regardless the complexity of the scenario going from simple geometries such as roofs [24] or columns [25] to complex geometries such as trees [26,27], buildings [24,28,29] or vehicles [30].…”

Section: Introductionmentioning

confidence: 99%

Automatic Evaluation of Photovoltaic Power Stations from High-Density RGB-T 3D Point Clouds

et al. 2017

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A low-cost unmanned aerial platform (UAV) equipped with RGB (Red, Green, Blue) and thermographic sensors is used for the acquisition of all the data needed for the automatic detection and evaluation of thermal pathologies on photovoltaic (PV) surfaces and geometric defects in the mounting on photovoltaic power stations. RGB imagery is used for the generation of a georeferenced 3D point cloud through digital image preprocessing, photogrammetric and computer vision algorithms. The point cloud is complemented with temperature values measured by the thermographic sensor and with intensity values derived from the RGB data in order to obtain a multidimensional product (5D: 3D geometry plus temperature and intensity on the visible spectrum). A segmentation workflow based on the proper integration of several state-of-the-art geomatic and mathematic techniques is applied to the 5D product for the detection and sizing of thermal pathologies and geometric defects in the mounting in the PV panels. It consists of a three-step segmentation procedure, involving first the geometric information, then the radiometric (RGB) information, and last the thermal data. No configuration of parameters is required. Thus, the methodology presented contributes to the automation of the inspection of PV farms, through the maximization of the exploitation of the data acquired in the different spectra (visible and thermal infrared bands). Results of the proposed workflow were compared with a ground truth generated according to currently established protocols and complemented with a topographic survey. The proposed methodology was able to detect all pathologies established by the ground truth without adding any false positives. Discrepancies in the measurement of damaged surfaces regarding established ground truth, which can reach the 5% of total panel surface for the visual inspection by an expert operator, decrease with the proposed methodology under the 2%. The geometric evaluation of the facilities presents discrepancies regarding the ground truth lower than one degree for angular parameters (azimuth and tilt) and lower than 0.05 m 2 for the area of each solar panel.

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Automatic Segmentation of Raw LIDAR Data for Extraction of Building Roofs

Cited by 127 publications

References 39 publications

Voxel- And Graph-Based Point Cloud Segmentation of 3d Scenes Using Perceptual Grouping Laws

Voxel- And Graph-Based Point Cloud Segmentation of 3d Scenes Using Perceptual Grouping Laws

Fusion of Lidar Data and Multispectral Imagery for Effective Building Detection Based on Graph and Connected Component Analysis

Automatic Evaluation of Photovoltaic Power Stations from High-Density RGB-T 3D Point Clouds

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