scholarly journals Strip Adjustment of Airborne LiDAR Data in Urban Scenes Using Planar Features by the Minimum Hausdorff Distance

Sensors ◽  
2019 ◽  
Vol 19 (23) ◽  
pp. 5131 ◽  
Author(s):  
Liu ◽  
Ma ◽  
Zhang ◽  
Cai ◽  
Ma
Keyword(s):  
Hausdorff Distance ◽  
Region Growing ◽  
Comparison Method ◽  
Binary Image ◽  
Airborne Lidar ◽  
Connected Components ◽  
Lidar Data ◽  
Building Roof ◽  
Dual Channel ◽  
Normal Vectors

In Airborne Light Detection and Ranging (LiDAR) data acquisition practice, discrepancies exist between adjacent strips even though careful system calibrations have been performed. A strip adjustment method using planar features acquired by the Minimum Hausdorff Distance (MHD) is proposed to eliminate these discrepancies. First, semi-suppressed fuzzy C-means and restricted region growing algorithms are used to extract buildings. Second, a binary image is generated from the minimum bounding rectangle that covers overlapping regions. Then, connected components labeling algorithm is applied to process the binary image to extract individual buildings. After that, building matching is performed based on MHD. Third, a coarse-to-fine approach is used to segment building roof planes. Then, plane matching is conducted under the constraints of MHD and normal vectors similarity. The last step is the calculation of the parameters based on Euclidean distance minimization between matched planes. Two different types of datasets, one of which was acquired by a dual-channel LiDAR system Trimble AX80, were selected to verify the proposed method. Experimental results show that the corresponding planar features that meet adjustment requirements can be successfully detected without any manual operations or auxiliary data or transformation of raw data, while the discrepancies between strips can be effectively eliminated. Although adjustment results of the proposed method slightly outperform the comparison alternative, the proposed method also has the advantage of processing the adjustment in a more automatic manner than the comparison method.

scholarly journals Roof Shape Classification from LiDAR and Satellite Image Data Fusion Using Supervised Learning

Sensors ◽  
2018 ◽  
Vol 18 (11) ◽  
pp. 3960 ◽  
Author(s):  
Jeremy Castagno ◽  
Ella Atkins
Keyword(s):  
Data Fusion ◽  
Satellite Image ◽  
Image Data ◽  
Unmanned Aircraft ◽  
Airborne Lidar ◽  
Support Vector ◽  
Lidar Data ◽  
Building Roof ◽  
Roof Structure ◽  
Roof Shape

Geographic information systems (GIS) provide accurate maps of terrain, roads, waterways, and building footprints and heights. Aircraft, particularly small unmanned aircraft systems (UAS), can exploit this and additional information such as building roof structure to improve navigation accuracy and safely perform contingency landings particularly in urban regions. However, building roof structure is not fully provided in maps. This paper proposes a method to automatically label building roof shape from publicly available GIS data. Satellite imagery and airborne LiDAR data are processed and manually labeled to create a diverse annotated roof image dataset for small to large urban cities. Multiple convolutional neural network (CNN) architectures are trained and tested, with the best performing networks providing a condensed feature set for support vector machine and decision tree classifiers. Satellite image and LiDAR data fusion is shown to provide greater classification accuracy than using either data type alone. Model confidence thresholds are adjusted leading to significant increases in models precision. Networks trained from roof data in Witten, Germany and Manhattan (New York City) are evaluated on independent data from these cities and Ann Arbor, Michigan.

scholarly journals AUTOMATIC EXTRACTION OF BUILDING ROOF PLANES FROM AIRBORNE LIDAR DATA APPLYING AN EXTENDED 3D RANDOMIZED HOUGH TRANSFORM

2016 ◽  
Vol III-3 ◽  
pp. 209-216 ◽  
Author(s):  
Evangelos Maltezos ◽  
Charalabos Ioannidis
Keyword(s):  
Hough Transform ◽  
Airborne Lidar ◽  
Computational Time ◽  
Experimental Comparison ◽  
Lidar Data ◽  
Building Roof ◽  
Additional Information ◽  
Randomized Hough Transform ◽  
Airborne Lidar Data ◽  
Scene Content

This study aims to extract automatically building roof planes from airborne LIDAR data applying an extended 3D Randomized Hough Transform (RHT). The proposed methodology consists of three main steps, namely detection of building points, plane detection and refinement. For the detection of the building points, the vegetative areas are first segmented from the scene content and the bare earth is extracted afterwards. The automatic plane detection of each building is performed applying extensions of the RHT associated with additional constraint criteria during the random selection of the 3 points aiming at the optimum adaptation to the building rooftops as well as using a simple design of the accumulator that efficiently detects the prominent planes. The refinement of the plane detection is conducted based on the relationship between neighbouring planes, the locality of the point and the use of additional information. An indicative experimental comparison to verify the advantages of the extended RHT compared to the 3D Standard Hough Transform (SHT) is implemented as well as the sensitivity of the proposed extensions and accumulator design is examined in the view of quality and computational time compared to the default RHT. Further, a comparison between the extended RHT and the RANSAC is carried out. The plane detection results illustrate the potential of the proposed extended RHT in terms of robustness and efficiency for several applications.

scholarly journals Synergy Between LiDAR and Image Data in Context of Building Extraction

2014 ◽  
Vol XL-1 ◽  
pp. 89-93
Author(s):  
A. P. Dal Poz
Keyword(s):  
Image Data ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Theoretical Studies ◽  
Building Extraction ◽  
Building Roof ◽  
Photogrammetric Method ◽  
Airborne Lidar Data

This paper compares the paradigms of LiDAR and aerophotogrammetry in the context of building extraction and briefly discusses a photogrammetric strategy for refining building roof polyhedrons previously extracted from LiDAR data. In general, empirical and theoretical studies have confirmed that LiDAR-based methodologies are more suitable in extracting planar roof faces and ridges of the roof, whereas the aerophotogrammetry are more suitable in extracting building roof outlines. In order to exemplify how to explore these properties, it is presented a photogrammetric method for refining 3D building roof contours extracted from airborne LiDAR data. Examples of application are provided for this refining approach.

scholarly journals FUSION OF LIDAR DATA AND MULTISPECTRAL IMAGERY FOR EFFECTIVE BUILDING DETECTION BASED ON GRAPH AND CONNECTED COMPONENT ANALYSIS

2015 ◽  
Vol XL-3/W2 ◽  
pp. 65-72 ◽  
Author(s):  
S. A. N. Gilani ◽  
M. Awrangjeb ◽  
G. Lu
Keyword(s):  
Component Analysis ◽  
Airborne Lidar ◽  
Connected Components ◽  
Lidar Data ◽  
Building Detection ◽  
Multispectral Imagery ◽  
Connected Component ◽  
Connected Component Analysis ◽  
Dense Vegetation ◽  
Black Pixel

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<sup>2</sup> and 10 m<sup>2</sup> 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<sup>2</sup> in area.

scholarly journals Roof Plane Segmentation from Airborne LiDAR Data Using Hierarchical Clustering and Boundary Relabeling

2020 ◽  
Vol 12 (9) ◽  
pp. 1363 ◽  
Author(s):  
Li Li ◽  
Jian Yao ◽  
Jingmin Tu ◽  
Xinyi Liu ◽  
Yinxuan Li ◽  
...  
Keyword(s):  
Hierarchical Clustering ◽  
Three Dimensional ◽  
Region Growing ◽  
Point Clouds ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Building Models ◽  
Novel Approach ◽  
Airborne Lidar Data ◽  
Seed Points

The roof plane segmentation is one of the key issues for constructing accurate three-dimensional building models from airborne light detection and ranging (LiDAR) data. Region growing is one of the most widely used methods to detect roof planes. It first selects one point or region as a seed, and then iteratively expands to neighboring points. However, region growing has two problems. The first problem is that it is hard to select the robust seed points. The other problem is that it is difficult to detect the accurate boundaries between two roof planes. In this paper, to solve these two problems, we propose a novel approach to segment the roof planes from airborne LiDAR point clouds using hierarchical clustering and boundary relabeling. For the first problem, we first extract the initial set of robust planar patches via an octree-based method, and then apply the hierarchical clustering method to iteratively merge the adjacent planar patches belonging to the same plane until the merging cost exceeds a predefined threshold. These merged planar patches are regarded as the robust seed patches for the next region growing. The coarse roof planes are generated by adding the non-planar points into the seed patches in sequence using region growing. However, the boundaries of coarse roof planes may be inaccurate. To solve this problem, namely, the second problem, we refine the boundaries between adjacent coarse planes by relabeling the boundary points. At last, we can effectively extract high-quality roof planes with smooth and accurate boundaries from airborne LiDAR data. We conducted our experiments on two datasets captured from Vaihingen and Wuhan using Leica ALS50 and Trimble Harrier 68i, respectively. The experimental results show that our proposed approach outperforms several representative approaches in both visual quality and quantitative metrics.

scholarly journals Regularization of Building Roof Boundaries from Airborne LiDAR Data Using an Iterative CD-Spline

2020 ◽  
Vol 12 (12) ◽  
pp. 1904
Author(s):  
Renato César dos Santos ◽  
Mauricio Galo ◽  
Ayman F. Habib
Keyword(s):  
Model Building ◽  
Real Data ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Building Roof ◽  
Building Models ◽  
The Many ◽  
Straight Lines ◽  
Airborne Lidar Data ◽  
Selection Of

Building boundaries play an essential role in many applications such as urban planning and production of 3D realistic views. In this context, airborne LiDAR data have been explored for the generation of digital building models. Despite the many developed strategies, there is no method capable of encompassing all the complexities in an urban environment. In general, the vast majority of existing regularization methods are based on building boundaries that are made up of straight lines. Therefore, the development of a strategy able to model building boundaries, regardless of their degree of complexity is of high importance. To overcome the limitations of existing strategies, an iterative CD-spline (changeable degree spline) regularization method is proposed. The main contribution is the automated selection of the polynomial function that best models each segment of the building roof boundaries. Conducted experiments with real data verified the ability of the proposed approach in modeling boundaries with different levels of complexities, including buildings composed of complex curved segments and point cloud with different densities, presenting Fscore and PoLiS around 95% and 0.30 m, respectively.

scholarly journals AUTOMATIC EXTRACTION OF BUILDING ROOF PLANES FROM AIRBORNE LIDAR DATA APPLYING AN EXTENDED 3D RANDOMIZED HOUGH TRANSFORM

2016 ◽  
Vol III-3 ◽  
pp. 209-216 ◽  
Author(s):  
Evangelos Maltezos ◽  
Charalabos Ioannidis
Keyword(s):  
Hough Transform ◽  
Airborne Lidar ◽  
Computational Time ◽  
Experimental Comparison ◽  
Lidar Data ◽  
Building Roof ◽  
Additional Information ◽  
Randomized Hough Transform ◽  
Airborne Lidar Data ◽  
Scene Content

This study aims to extract automatically building roof planes from airborne LIDAR data applying an extended 3D Randomized Hough Transform (RHT). The proposed methodology consists of three main steps, namely detection of building points, plane detection and refinement. For the detection of the building points, the vegetative areas are first segmented from the scene content and the bare earth is extracted afterwards. The automatic plane detection of each building is performed applying extensions of the RHT associated with additional constraint criteria during the random selection of the 3 points aiming at the optimum adaptation to the building rooftops as well as using a simple design of the accumulator that efficiently detects the prominent planes. The refinement of the plane detection is conducted based on the relationship between neighbouring planes, the locality of the point and the use of additional information. An indicative experimental comparison to verify the advantages of the extended RHT compared to the 3D Standard Hough Transform (SHT) is implemented as well as the sensitivity of the proposed extensions and accumulator design is examined in the view of quality and computational time compared to the default RHT. Further, a comparison between the extended RHT and the RANSAC is carried out. The plane detection results illustrate the potential of the proposed extended RHT in terms of robustness and efficiency for several applications.

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