Zhang Yinchao

Boresight Calibration of Airborne LiDAR System Without Ground Control Points

This letter proposes a new method for boresight misalignment calibration of the charge-coupled device (CCD) camera which is one component of an airborne light detection and ranging (LiDAR) system without ground control points (GCPs). In the calibration, tie points in overlapping areas are first selected, and then, a multibaseline forward intersection is used for calculating object coordinates of these points. In the intersection, exterior elements of the CCD camera are obtained directly from positioning and orientation system (POS) data of the LiDAR system, which are error contaminated mainly due to the unparallel relation between the frameworks of the inertial measurement unit of the POS and the CCD camera. Elevation values of the ground points are then refined by those obtained from LiDAR point clouds by interpolation, which can be considered to be more accurate than those obtained by multibaseline forward intersection. Through projecting the ground points with refined elevation values into the image space by collinear equations and minimizing distances between the image points selected manually and those projected from ground points, the boresight misalignment is removed effectively. Therefore, the proposed method without GCPs in the whole process is more flexible than other traditional photogrammetric ways.

Capability of Raman lidar for monitoring the variation of atmospheric CO2 profile

Lidar (Light detection and ranging) has special capabilities for remote sensing of many different behaviours of the atmosphere. One of the techniques which show a great deal of promise for several applications is Raman scattering. The detecting capability, including maximum operation range and minimum detectable gas concentration is one of the most significant parameters for lidar remote sensing of pollutants. In this paper, based on the new method for evaluating the capabilities of a Raman lidar system, we present an evaluation of detecting capability of Raman lidar for monitoring atmospheric CO2 in Hefei. Numerical simulations about the influence of atmospheric conditions on lidar detecting capability were carried out, and a conclusion can be drawn that the maximum difference of the operation ranges caused by the weather conditions alone can reach about 0.4 to 0.5km with a measuring precision within 30ppmv. The range of minimum detectable concentration caused by the weather conditions alone can reach about 20 to 35 ppmv in vertical direction for 20000 shots at a distance of 1 km on the assumption that other parameters are kept constant. The other corresponding parameters under different conditions are also given. The capability of Raman lidar operated in vertical direction was found to be superior to that operated in horizontal direction. During practical measurement with the Raman lidar whose hardware components were fixed, aerosol scattering extinction effect would be a significant factor that influenced the capability of Raman lidar. This work may be a valuable reference for lidar system designing, measurement accuracy improving and data processing.

Airborne LiDAR strip adjustment based on conjugate linear features

An airborne LiDAR is a complex multi-sensor integrated system. The existence of systematic errors will lead to discrepancies between overlapping strips. This paper presents a algorithm to detect and adjust such discrepancies and creat a seamless dataset. Due to the irregular nature of the LiDAR data, Linear features are used and a point-to-point correspondence are built by extracting the endpoints of conjugate linear features. Firstly, linear features are extracted from the point clouds in overlapping strips. Secondly, endpoints of these linear features are obtained and tie points matching are also accomplished. Further, an improved Bursa model is used to adjust overlapping strips through a least squares matching procedure. At last, an experiment with real datasets is carried out to verify that the methodology is effective and efficient. The root mean square error (RMSE) between conjugate points is used to evaluate the accuracy after adjustment.