Evaluation and Improvement of Lidar Performance Based on Temporal and Spatial Variance Calculation

Abstract

Poisson distributions have the characteristic of equality between their variance and mean values. By constructing a calculation model of the temporal variance and spatial variance, the relationship between the variance and mean values of lidar analog data and photon-counting data can be analyzed. The calculation results show that the photon-counting data from far field have the distribution property of equality between the variances and the corresponding mean values, while the analog data for the whole probing traces do not. In this paper, by analyzing the distribution properties of the spatial variance and temporal variance of lidar data, the dead time of photon-counting data was estimated, and the threshold voltage of the photon-counting system and the linear working range of photomultiplier tube were evaluated. The results show that the linear working range of the high voltage for the photomultiplier tube in the ultraviolet elastic scanning lidar is between −500 V and −1000 V, and the dead time and threshold voltage of the photon-counting system in the Licel transient recorder are 3.488 ns and 1.20 mV, respectively. Meanwhile, a novel gluing method between analog data and photon-counting data is presented, based on the calculation results of the variance distribution of lidar data. The linear transfer coefficients were determined by minimizing the differences between the variance and mean of the transformed photon-counting data in the near filed with high signal to noise ratio. The glued data were distributed to express the atmospheric conditions uniformly.

The Lidargrammetric Model Deformation Method for Altimetric UAV-ALS Data Enhancement

“…More complex and advanced solutions for the assessment and enhancement of accuracy are proposed on the basis of specifically designed GCPs [16], linear features [17], roofs [18] or other geometric features [19]. The algorithms use the methods known from photogrammetric block adjustment of independent models [20], iterative closest point (ICP) [13], high accuracy time interval measurement methods of pulse of LiDAR (Light Detecting and Ranging) [15] and the calculation models for temporal variance and spatial variance for taking into account the physical conditions of the atmosphere [21]. Recently, LiDAR platforms for unmanned aerial vehicles (UAVs) have gained increasing popularity, whilst their accuracy is assessed on test fields [22] for digital terrain model (DTM) generation [23] or for specific purposes like forest analysis [24][25][26][27].…”

Gluing Atmospheric Lidar Signals Based on an Improved Gray Wolf Optimizer

“…Both works provided relatively reliable gluing regions, but it should be noted that iteration with a fixed step size is mechanical in the algorithm, limiting the ability to search for the optimal gluing region. In addition, gluing methods based on the lamp mapping technique, statistical principles and spatiotemporal variance have been proposed [12][13][14], but they are very complex to operate or have poor applicability.…”