scholarly journals Multispectral LiDAR data for land cover classification of urban areas

2021 ◽  
Author(s):  
Salem Morsy ◽  
Ahmed Shaker ◽  
Ahmed El-Rabbany
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Energy Intensity ◽  
Vegetation Indices ◽  
Land Cover Classification ◽  
Lidar Data ◽  
Radiometric Correction ◽  
Correction Model ◽  
Normalized Difference Vegetation Indices

Airborne Light Detection And Ranging (LiDAR) systems usually operate at a monochromatic wavelength measuring the range and the strength of the reflected energy (intensity) from objects. Recently, multispectral LiDAR sensors, which acquire data at different wavelengths, have emerged. This allows for recording of a diversity of spectral reflectance from objects. In this context, we aim to investigate the use of multispectral LiDAR data in land cover classification using two different techniques. The first is image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood lassifier is applied. The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs) computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass. An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively. A radiometric correction model is also applied to the intensity data in order to remove the attenuation due to the system distortion and terrain height variation. The classification process is then repeated, and the results demonstrate that there are no significant improvements achieved in the overall accuracy. Keywords: multispectral LiDAR; land cover; ground filtering; NDVI; radiometric correction

scholarly journals Multispectral LiDAR data for land cover classification of urban areas

2021 ◽  
Author(s):  
Salem Morsy ◽  
Ahmed Shaker ◽  
Ahmed El-Rabbany
Keyword(s):  
Land Cover ◽  
Urban Areas ◽  
Energy Intensity ◽  
Vegetation Indices ◽  
Land Cover Classification ◽  
Lidar Data ◽  
Radiometric Correction ◽  
Correction Model ◽  
Normalized Difference Vegetation Indices

Airborne Light Detection And Ranging (LiDAR) systems usually operate at a monochromatic wavelength measuring the range and the strength of the reflected energy (intensity) from objects. Recently, multispectral LiDAR sensors, which acquire data at different wavelengths, have emerged. This allows for recording of a diversity of spectral reflectance from objects. In this context, we aim to investigate the use of multispectral LiDAR data in land cover classification using two different techniques. The first is image-based classification, where intensity and height images are created from LiDAR points and then a maximum likelihood lassifier is applied. The second is point-based classification, where ground filtering and Normalized Difference Vegetation Indices (NDVIs) computation are conducted. A dataset of an urban area located in Oshawa, Ontario, Canada, is classified into four classes: buildings, trees, roads and grass. An overall accuracy of up to 89.9% and 92.7% is achieved from image classification and 3D point classification, respectively. A radiometric correction model is also applied to the intensity data in order to remove the attenuation due to the system distortion and terrain height variation. The classification process is then repeated, and the results demonstrate that there are no significant improvements achieved in the overall accuracy. Keywords: multispectral LiDAR; land cover; ground filtering; NDVI; radiometric correction

scholarly journals Accuracy Enhancement for Land Cover Classification Using LiDAR and Multitemporal Sentinel 2 Images in a Forested Watershed

Proceedings ◽  
2018 ◽  
Vol 2 (20) ◽  
pp. 1280 ◽  
Author(s):  
Laura Fragoso-Campón ◽  
Elia Quirós ◽  
Julián Mora ◽  
José Antonio Gutiérrez ◽  
Pablo Durán-Barroso
Keyword(s):  
Land Cover ◽  
Vegetation Cover ◽  
Spectral Response ◽  
Vegetation Indices ◽  
Land Cover Classification ◽  
Lidar Data ◽  
Accuracy Enhancement ◽  
Multispectral Data ◽  
Sentinel 2

Mapping land cover with high accuracy has become a reality with the application of current remote sensing techniques. Due to the specific spectral response of the vegetation, soil and vegetation indices are adequate tools to help in the discrimination of land uses. Additionally, the accuracy of satellite imagery classification can be improved using multitemporal series combined with LiDAR data. This datafusion takes advantage of the information provided by LiDAR for the vegetation cover density, and the capability of multispectral data to detect the type of vegetation. The main goal of this study is to analyze the accuracy enhancement in land cover classification of two forested watersheds when using datafusion of annual time series of Sentinel-2 images complemented with low density LiDAR. The obtained results show that overall accuracy is better if LiDAR data is included in the classification. This improvement can be a significant issue in land cover classification of forest watershed due to relationship and influence that vegetation cover has on runoff estimation.

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.

scholarly journals Radiometric correction and normalization of airborne LiDAR intensity data for land cover classification

2021 ◽  
Author(s):  
Wai Yeung Yan
Keyword(s):  
Land Cover ◽  
Laser Energy ◽  
Experimental Testing ◽  
Processing System ◽  
Land Cover Classification ◽  
Gaussian Mixture ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Intensity Data ◽  
Radiometric Correction

Airborne Light Detection And Ranging (LiDAR) has been used extensively to model the topography of the Earth surface by emitting laser pulses and measuring the distance (range) between the LiDAR sensor and the illuminated object as well as the backscattered laser energy (intensity). Nowadays, airborne LiDAR systems operating in near-infrared spectrum are also gaining a high level of interest for surface classification and object recognition. Nevertheless, due to the system- and environmental- induced distortions, airborne LiDAR intensity data requires certain correction and normalization schemes to maximize the benefits from the collected data. The first part of the thesis presents a correction model for airborne LiDAR intensity data based on the radar (range) equation. To fill the gap in current research, the thesis introduces a set of correction parameters considering the attenuation due to atmospheric absorption and scattering which have not been previously considered. The thesis further derives a set of equations to compute the laser incidence angle based on the LiDAR data point cloud and GPS trajectory. In the second part of the thesis, a normalization model is proposed to adjust the radiometric misalignment amongst overlapping airborne LiDAR intensity data. The model is built upon the use of a Gaussian mixture modeling technique for fitting the intensity histogram which can then be partitioned into several sub-histograms. Finally, sub-histogram equalization is applied to calibrate the LiDAR intensity data. To evaluate the effects of the proposed methods, a LiDAR dataset covering an urban area with three different scans was used for experimental testing. The results showed that the coefficient of variance of five land cover features were significantly reduced by 70% to 82% and 33% to 80% after radiometric correction and radiometric normalization, respectively. Land cover classification was conducted on the LiDAR intensity data where accuracy improvements of up to 15% and 16.5% were found on the classification results using the radiometrically corrected intensity data, and radiometrically corrected and normalized intensity data, respectively. With the improved land cover homogeneity and classification accuracy, the effectiveness of the proposed approach was demonstrated. The outcome of the thesis fills the gap in existing airborne LiDAR research and paves the way for the future development of LiDAR data processing system.

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