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.

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.

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 REDUCTION OF STRIPING NOISE IN OVERLAPPING LIDAR INTENSITY DATA BY RADIOMETRIC NORMALIZATION

2016 ◽  
Vol XLI-B1 ◽  
pp. 151-156
Author(s):  
Wai Yeung Yan ◽  
Ahmed Shaker
Keyword(s):  
Land Cover ◽  
Environmental Parameters ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Intensity Data ◽  
Joint Histogram ◽  
Before And After ◽  
Range Equation ◽  
Parallel Strips ◽  
Airborne Lidar Data

To serve seamless mapping, airborne LiDAR data are usually collected with multiple parallel strips with one or two cross strip(s). Nevertheless, the overlapping regions of LiDAR data strips are usually found with unbalanced intensity values, resulting in the appearance of stripping noise. Despite that physical intensity correction methods are recently proposed, some of the system and environmental parameters are assumed as constant or not disclosed, leading to such an intensity discrepancy. This paper presents a new normalization technique to adjust the radiometric misalignment found in the overlapping LiDAR data strips. The normalization technique is built upon a second-order polynomial function fitted on the joint histogram plot, which is generated with a set of pairwise closest data points identified within the overlapping region. The method was tested on Teledyne Optech’s Gemini dataset (at 1064 nm wavelength), where the LiDAR intensity data were first radiometrically corrected based on the radar (range) equation. Five land cover features were selected to evaluate the coefficient of variation (<i>cv</i>) of the intensity values before and after implementing the proposed method. Reduction of <i>cv</i> was found by 19% to 59% in the Gemini dataset, where the striping noise was significantly reduced in the radiometrically corrected and normalized intensity data. The Gemini dataset was also used to conduct land cover classification, and the overall accuracy yielded a notable improvement of 9% to 18%. As a result, LiDAR intensity data should be pre-processed with radiometric correction and normalization prior to any data manipulation.

scholarly journals Automatic CORINE land cover classification from airborne LIDAR data

2018 ◽  
Vol 126 ◽  
pp. 186-194 ◽  
Author(s):  
Jesús Balado ◽  
Pedro Arias ◽  
Lucía Díaz-Vilariño ◽  
Luis M. González-deSantos
Keyword(s):  
Land Cover ◽  
Land Cover Classification ◽  
Airborne Lidar ◽  
Lidar Data ◽  
Corine Land Cover ◽  
Airborne Lidar Data
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