Abstract
Intensity value based point cloud segmentation has received less attention because the intensity value of the terrestrial laser scanner is usually altered by receiving optics/hardware or the internal propriety software, which is unavailable to the end user. We offer a solution by assuming the terrestrial laser scanners are stable and the behavior of the intensity value can be characterized. Then, it is possible to use the intensity value for segmentation by observing its behavior, i.e., intensity value variation, pattern and presence of location of intensity values, etc. In this study, experiment results for characterizing the intensity data of planar surfaces collected by ILRIS3D, a terrestrial laser scanner, are reported. Two intensity formats, grey and raw, are employed by ILRIS3D. It is found from the experiment results that the grey intensity has less variation; hence it is preferable for point cloud segmentation. A warm-up time of approximate 1.5 hours is suggested for more stable intensity data. A segmentation method based on the visual cues of the intensity images sequence, which contains consecutive intensity images, is proposed in order to segment the 3D laser points of ILRIS3D. This method is unique to ILRIS3D data and does not require radiometric calibration.
Highlights
LIght Detection And Ranging (LIDAR) is an active remote sensing system that uses a pulse or continuous-wave laser to gather 3D information of the terrains and buildings at day or night [1]
LIDAR point cloud can further be used for 3D building modeling [2], vegetation classification [3,4,5], etc
Many of the algorithms rely solely on the geometric properties of the remote sensed objects represented by the LIDAR point cloud [2,3]
Summary
LIght Detection And Ranging (LIDAR) is an active remote sensing system that uses a pulse or continuous-wave laser to gather 3D information of the terrains and buildings at day or night [1]. The. LIDAR point cloud can further be used for 3D building modeling [2], vegetation classification [3,4,5], etc. Many of the algorithms rely solely on the geometric properties of the remote sensed objects represented by the LIDAR point cloud [2,3]. Others employ the LIDAR intensity to refine data processing workflow [4,5,6]. For TLS (terrestrial laser scanner), or ground-based LIDAR, the intensity is usually recorded as an extra variable in addition to the 3D position information. The LIDAR intensity is a function of the reflectance and the texture of the target surface, the distance between the laser and the target, the angle of incidence of the laser beam impinging on the target surface, the transmitted power of laser, and the atmosphere attenuation coefficient of the air through which the beam has traveled [4,6,7,8,9,10,11,12,13,14,15,16]
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