Paper Info
Title
Characterizing Radiometric Attributes of Point Cloud Using a Normalized Reflective Factor Derived From Small Footprint LiDAR Waveform
Abstract
This paper presents a reflectance-like coefficient, normalized reflective factor (NRF) to characterize the radiometric attributes of point cloud generated from small footprint light detection and ranging (LiDAR) waveform data. The NRF is defined as a normalized ratio between the energy of emitted laser beam and the peak in return waveform in conjunction with the atmospheric attenuation and observation geometry. Based on the Gaussian parameters of the emitted and return waveforms, NRF is calculated with an empirical atmospheric model and user-defined standard observation geometry. To correct the radiometric measurement of point cloud in multipeak waveform, a semi-physical-based method is adopted to enhance the NRF of point cloud generated from multipeak waveform. Experiments are conducted with small footprint LiDAR waveform data acquired by RIEGL LMS-Q560. A curve-fitting-based approach is applied to decompose LiDAR waveform into three-dimensional (3-D) coordinates of point cloud, and the NRF are calculated using the Gaussian parameters of both emitted and return waveforms. The visualization of the radiometric attributes of point cloud data is carried out over the overlapping areas between different flight strips, it suggests that the NRF over overlapping area is much smooth than the normalized intensity. Quantitative comparison with Hyperion data indicates that the NRF has much higher correlation with surface reflectance than the normalized intensity data. Standard deviations of NRF and the normalized intensity of different land cover patches are analyzed to assess the homogeneity of the radiometric data. It is observed that NRF has less variability than the normalized intensity within the same land cover patches. Point cloud of two sample trees is also selected to assess the performance of the “sub-footprint” effect correction. It is observed that the proposed approach reduced the variability of radiometric attributes over tree canopies with increa- ing NRF values; which means the “sub-footprint” effect is mitigated. In summary, the proposed NRF can serve as a promising indicator to characterize radiometric attribute of LiDAR point cloud.
Year
DOI
Venue
2015
10.1109/JSTARS.2014.2354014
Selected Topics in Applied Earth Observations and Remote Sensing, IEEE Journal of  
Keywords
Field
DocType
optical radar,remote sensing by laser beam,gaussian parameters,hyperion data,lidar waveform data,riegl lms-q560,atmospheric attenuation,curve-fitting-based approach,emitted laser beam energy,empirical atmospheric model,land cover patches,normalized intensity data,normalized ratio,normalized reflective factor,observation geometry,point cloud 3-d coordinates,radiometric attributes,radiometric data homogeneity,reflectance-like coefficient,small footprint lidar waveform,sub-footprint effect correction,user-defined standard observation geometry,correction,intensity,normalized reflective factor (nrf),small footprint light detection and ranging (lidar),waveform,laser radar,radiometry,remote sensing
Normalization (statistics),Waveform,Remote sensing,Atmospheric model,Radiometry,Lidar,Attenuation,Point cloud,Standard deviation,Mathematics
Journal
Volume
Issue
ISSN
8
2
1939-1404
Citations 
PageRank 
References 
4
0.59
12
Authors
6
Name
Order
Citations
PageRank
yuchu qin141.94
Wei Yao216224.56
Tuong Thuy Vu3375.47
shihua li441.94
Zheng Niu514422.34
Yifang Ban619422.83