scholarly journals Using multispectral airborne LiDAR data for land/water discrimination: a case study at Lake Ontario, Canada

2021 ◽  
Author(s):  
Salem Morsy ◽  
Ahmed Shaker ◽  
Ahmed El-Rabbany
Keyword(s):  
New Technology ◽  
Region Growing ◽  
Lake Ontario ◽  
Coastal Areas ◽  
Accurate Solution ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Seeded Region Growing ◽  
532 Nm ◽  
Land Water

Coastal areas are environmentally sensitive and are affected by nature events and human activities. Land/water interaction in coastal areas changes over time and, therefore, requires accurate detection and frequent monitoring. Multispectral Light Detection and Ranging (LiDAR) systems, which operate at different wavelengths, have become available. This new technology can provide an effective and accurate solution for the determination of the land/water interface. In this context, we aim to investigate a set of point features based on elevation, intensity, and geometry for this application, followed by a presentation of an unsupervised land/water discrimination method based on seeded region growing algorithm. The multispectral airborne LiDAR sensor, the Optech Titan, was used to acquire LiDAR data at three wavelengths (1550, 1064, and 532 nm) of a study area covering part of Lake Ontario in Scarborough, Canada for testing the discrimination methods. The elevation- and geometry-based features achieved an average overall accuracy of 75.1% and 74.2%, respectively, while the intensity-based features achieved 63.9% accuracy. The region growing method succeeded in discriminating water from land with more than 99% overall accuracy, and the land/water boundary was delineated with an average root mean square error of 0.51 m. The automation of this method is restricted by having double returns from water bodies at the 532 nm wavelength.

scholarly journals Using multispectral airborne LiDAR data for land/water discrimination: a case study at Lake Ontario, Canada

2021 ◽  
Author(s):  
Salem Morsy ◽  
Ahmed Shaker ◽  
Ahmed El-Rabbany
Keyword(s):  
New Technology ◽  
Region Growing ◽  
Lake Ontario ◽  
Coastal Areas ◽  
Accurate Solution ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Seeded Region Growing ◽  
532 Nm ◽  
Land Water

Coastal areas are environmentally sensitive and are affected by nature events and human activities. Land/water interaction in coastal areas changes over time and, therefore, requires accurate detection and frequent monitoring. Multispectral Light Detection and Ranging (LiDAR) systems, which operate at different wavelengths, have become available. This new technology can provide an effective and accurate solution for the determination of the land/water interface. In this context, we aim to investigate a set of point features based on elevation, intensity, and geometry for this application, followed by a presentation of an unsupervised land/water discrimination method based on seeded region growing algorithm. The multispectral airborne LiDAR sensor, the Optech Titan, was used to acquire LiDAR data at three wavelengths (1550, 1064, and 532 nm) of a study area covering part of Lake Ontario in Scarborough, Canada for testing the discrimination methods. The elevation- and geometry-based features achieved an average overall accuracy of 75.1% and 74.2%, respectively, while the intensity-based features achieved 63.9% accuracy. The region growing method succeeded in discriminating water from land with more than 99% overall accuracy, and the land/water boundary was delineated with an average root mean square error of 0.51 m. The automation of this method is restricted by having double returns from water bodies at the 532 nm wavelength.

scholarly journals Land/Water Discrimination And Land Cover Classification Using Multispectral Airborne Lidar Data

2021 ◽  
Author(s):  
Salem Wagih Salem Morsy
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Region Growing ◽  
Point Clouds ◽  
Land Cover Classification ◽  
Lidar Data ◽  
Coastal Zones ◽  
532 Nm ◽  
Land Water

Multispectral airborne Light Detection And Ranging (LiDAR) systems are currently available. Optech Titan is an example of these systems, which acquires LiDAR point clouds at three independent wavelengths (1550, 1064 and 532 nm) from Earth’s surface. This dissertation aims to use the radiometric information (i.e., intensity) of the Optech Titan LiDAR data along with the geometric information (e.g., height) for land/water discrimination in coastal zones and land cover classification of urban areas. A set of point features based on elevation, intensity, and geometry was extracted and evaluated for land/water discrimination in coastal zones. In addition, an automated land/water discrimination approach based on seeded region growing algorithm was presented. Two data subsets were tested at Lake Ontario and Tobermory Harbour in Ontario, Canada. The elevation and geometry-based features achieved average overall accuracies of 72.8% - 83.3% and 69.9% -74.4%, respectively, while the intensity-based features achieved an average overall accuracy of 59.0% - 63.4%. The region growing method achieved an average overall accuracy of more than 99%, and the automation of this method is restricted by having double returns from water bodies at the 532 nm wavelength. A hierarchal point-based classification approach was presented for land cover classification of urban areas. The collected point clouds at the three wavelengths were first merged and three intensity values were estimated for each LiDAR point, followed by three-level classification approach. First, a ground filtering method was applied to separate non-ground from ground points. Second, three normalized difference vegetation indices (NDVIs) were computed, followed by NDVIs’ histograms construction. A multivariate Gaussian decomposition (MVGD) was then used to divide those histograms into buildings or trees from non-ground and roads or grass from ground points. Third, classes such as power lines, swimming pools and different types of trees were labeled based on their spectral characteristics. Three data subsets were tested representing different complexity of urban areas in Oshawa, Ontario, Canada. It is shown that the presented approach has achieved an overall accuracy up to 93.0%, which increased to more than 99% by considering the spatial coherence of the LiDAR point clouds.

scholarly journals Land/Water Discrimination And Land Cover Classification Using Multispectral Airborne Lidar Data

2021 ◽  
Author(s):  
Salem Wagih Salem Morsy
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Region Growing ◽  
Point Clouds ◽  
Land Cover Classification ◽  
Lidar Data ◽  
Coastal Zones ◽  
532 Nm ◽  
Land Water

Multispectral airborne Light Detection And Ranging (LiDAR) systems are currently available. Optech Titan is an example of these systems, which acquires LiDAR point clouds at three independent wavelengths (1550, 1064 and 532 nm) from Earth’s surface. This dissertation aims to use the radiometric information (i.e., intensity) of the Optech Titan LiDAR data along with the geometric information (e.g., height) for land/water discrimination in coastal zones and land cover classification of urban areas. A set of point features based on elevation, intensity, and geometry was extracted and evaluated for land/water discrimination in coastal zones. In addition, an automated land/water discrimination approach based on seeded region growing algorithm was presented. Two data subsets were tested at Lake Ontario and Tobermory Harbour in Ontario, Canada. The elevation and geometry-based features achieved average overall accuracies of 72.8% - 83.3% and 69.9% -74.4%, respectively, while the intensity-based features achieved an average overall accuracy of 59.0% - 63.4%. The region growing method achieved an average overall accuracy of more than 99%, and the automation of this method is restricted by having double returns from water bodies at the 532 nm wavelength. A hierarchal point-based classification approach was presented for land cover classification of urban areas. The collected point clouds at the three wavelengths were first merged and three intensity values were estimated for each LiDAR point, followed by three-level classification approach. First, a ground filtering method was applied to separate non-ground from ground points. Second, three normalized difference vegetation indices (NDVIs) were computed, followed by NDVIs’ histograms construction. A multivariate Gaussian decomposition (MVGD) was then used to divide those histograms into buildings or trees from non-ground and roads or grass from ground points. Third, classes such as power lines, swimming pools and different types of trees were labeled based on their spectral characteristics. Three data subsets were tested representing different complexity of urban areas in Oshawa, Ontario, Canada. It is shown that the presented approach has achieved an overall accuracy up to 93.0%, which increased to more than 99% by considering the spatial coherence of the LiDAR point clouds.

Mapping piping plover (Charadrius melodus melodus) habitat in coastal areas using airborne lidar data

2007 ◽  
Vol 33 (6) ◽  
pp. 519-533 ◽  
Author(s):  
R. Goodale ◽  
C. Hopkinson ◽  
D. Colville ◽  
D. Amirault-Langlais
Keyword(s):  
Coastal Areas ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Piping Plover ◽  
Charadrius Melodus ◽  
Airborne Lidar Data

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 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 WATER MAPPING USING MULTISPECTRAL AIRBORNE LIDAR DATA

2018 ◽  
Vol XLII-3 ◽  
pp. 2047-2052 ◽  
Author(s):  
W. Y. Yan ◽  
A. Shaker ◽  
P. E. LaRocque
Keyword(s):  
Water Surface ◽  
Experimental Testing ◽  
Airborne Lidar ◽  
Training Data ◽  
Lidar Data ◽  
Automatic Data ◽  
Water Classification ◽  
Near Shore ◽  
Airborne Lidar Data ◽  
Land Water

This study investigates the use of the world’s first multispectral airborne LiDAR sensor, Optech Titan, manufactured by Teledyne Optech to serve the purpose of automatic land-water classification with a particular focus on near shore region and river environment. Although there exist recent studies utilizing airborne LiDAR data for shoreline detection and water surface mapping, the majority of them only perform experimental testing on clipped data subset or rely on data fusion with aerial/satellite image. In addition, most of the existing approaches require manual intervention or existing tidal/datum data for sample collection of training data. To tackle the drawbacks of previous approaches, we propose and develop an automatic data processing workflow for land-water classification using multispectral airborne LiDAR data. Depending on the nature of the study scene, two methods are proposed for automatic training data selection. The first method utilizes the elevation/intensity histogram fitted with Gaussian mixture model (GMM) to preliminarily split the land and water bodies. The second method mainly relies on the use of a newly developed scan line elevation intensity ratio (SLIER) to estimate the water surface data points. Regardless of the training methods being used, feature spaces can be constructed using the multispectral LiDAR intensity, elevation and other features derived from these parameters. The comprehensive workflow was tested with two datasets collected for different near shore region and river environment, where the overall accuracy yielded better than 96 %.

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