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:: Volume 8, Issue 2 (12-2018) ::
JGST 2018, 8(2): 99-113 Back to browse issues page
Increasing the Density of Airborne LiDAR Point Cloud by Full Waveform Analysis
M. Babadi , M. Sattari
Abstract:   (105 Views)
In recent years, Light Detection and Ranging (LiDAR) systems, as one of the active remote sensing laser technology, have become one of the most promising tools for measurements of Earth surface and its modeling. With the advent of airborne and satellite LiDAR systems, it has been possible to extract information and parameters related to the vertical structure of the targets, especially trees, while earlier, this was not possible by the use of passive remote sensing data such as multispectral images. Point cloud generated by this sensors provides precise information of the targets on the laser path and their vertical distribution. Some of the applications of these systems are forest management, measurement of forest parameters, Digital Terrain Model generation, sea depth determination, the polar ice thickness determination, 3D city modeling, bridge and power line detection, costal mapping, open cast mapping and land cover classification. Due to the fact that the output of primary LiDAR systems (discrete LiDAR systems) is merely point cloud and is less associated with the intensity recorded for them, there are some limitations in some of its applications such as tree species classification and single tree detection especially in densely forested areas. Since 2004, new commercial airborne laser scanners, namely full waveform LiDAR Systems, have appeared. In recent years, recording the full waveform LiDAR data by these systems has made it possible to rectify some of the weaknesses of the discrete LiDAR systems such as low density of generated point cloud and their limitation in classification tasks; these systems made it possible to classify different tree species and classify targets more precisely by providing features of return waveforms such as amplitude and intensity of return pulses. One of the challenges related to these data is how to decompose return waveforms and generate point cloud and additional information related to waveforms. A great deal of research has been done on using discrete LiDAR data and its applications in forest management and 3D city modeling  in Iran; However, full waveform LiDAR data, the process of decomposing LiDAR waveforms to point cloud and different decomposition methods are still unknown. Some of the most important reasons for this matter are unavailability of these data, lack of enough knowledge about the nature of this type of data, Lack of software especially free ones for processing them and the lack of information from commercial firms producing LiDAR sensors. In this research LiDAR waveforms of a forested area have been investigated and it has been tried to show how to decompose raw full waveform LiDAR data to 3D point cloud and extract information and features related to each return waveform. In addition in this research, the results of point cloud generated from full waveform LiDAR data is compared with point cloud acquired from LiDAR sensor to show how the density of LiDAR point cloud can be increased by full waveform analysis. Finally, the generated LiDAR point cloud is visualized based on its extracted features such as amplitude, width, intensity and number of return to show their application in clustering and classification tasks.
Keywords: Full Waveform LiDAR System, Return Waveform Analysis, Point Cloud Generation, LiDAR Features Generation
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Type of Study: Research | Subject: Photo&RS
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Babadi M, Sattari M. Increasing the Density of Airborne LiDAR Point Cloud by Full Waveform Analysis. JGST. 2018; 8 (2) :99-113
URL: http://jgst.issge.ir/article-1-727-en.html

Volume 8, Issue 2 (12-2018) Back to browse issues page
نشریه علمی پژوهشی علوم و فنون نقشه برداری Journal of Geomatics Science and Technology