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.
Integration of Heterogeneous Features from Co-registered Hyperspectral and LiDAR Data for Land Cover Classification
