Airborne light detection and ranging (LiDAR) system calibration is a crucial procedure for ensuring the accuracy of point data. A common practice is to use conjugate planar patches to recover systematic parameters based on coplanar constraints and to use planes with different orientations to decrease the correlations between the systematic errors. When there are not sufficient planar patches and the configuration of planar patches is not optimal, it is difficult to guarantee the reliability of the estimated system parameters. Based on the analyses of the bore-sight angle effects, we find that not only the orientations but also the distribution of planar patches play an important role in the calibration procedure. We propose an improved method for bore-sight calibration based on the principles of symmetry of coordinate offsets and low correlations between bore-sight angles. Comparisons of the experimental results of bore-sight angle calibration suggest that the proposed configuration of conjugate planar patches can decrease the correlations between bore-sight angles and improve the reliability of calibration results. The optical results obtained from four gable-roof buildings are very close to the results calculated by the RiProcess software with a deviation of about 0.001 deg.
Airborne Light Detection and Ranging (LiDAR) is an active remote sensing technology which can acquire the
topographic information efficiently. It can record the accurate 3D coordinates of the targets and also the signal intensity
(the amplitude of backscattered echoes) which represents reflectance characteristics of targets. The intensity data has
been used in land use classification, vegetation fractional cover and leaf area index (LAI) estimation. Apart from the
reflectance characteristics of the targets, the intensity data can also be influenced by many other factors, such as flying
height, incident angle, atmospheric attenuation, laser pulse power and laser beam width. It is therefore necessary to
calibrate intensity values before further applications. In this study, we analyze the factors affecting LiDAR intensity
based on radar range equation firstly, and then applying the intensity calibration method, which includes the
sensor-to-target distance and incident angle, to the laser intensity data over the study area. Finally the raw LiDAR
intensity and normalized intensity data are used for land use classification along with LiDAR elevation data respectively.
The results show that the classification accuracy from the normalized intensity data is higher than that from raw LiDAR
intensity data and also indicate that the calibration of LiDAR intensity data is necessary in the application of land use
classification.
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