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Uncertainty analysis of the absolute radiometric calibration of full waveform airborne LiDAR

  • role： First author第一作者
  • Affiliation：

    School of Information Science and Technology, Beijing Forestry University, Beijing 100083, China

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

  • Email：luhao@bjfu.edu.cn
  • Introduction：卢昊，1990年生,男,讲师,研究方向为全波形机载激光雷达信号处理、林业遥感。E-mail: luhao@bjfu.edu.cn
LU Hao12,  
  • role： Corresponding author通信作者
  • Affiliation：

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

  • Email：pangy@ifrit.ac.cn
  • Introduction：庞勇，1976年生，男,研究员，研究方向为地基、机载、星载激光雷达林业应用。E-mail: pangy@ifrit.ac.cn
PANG Yong2*,  
  • Affiliation：

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

LI Zengyuan2,  
  • Affiliation：

    School of Engineering, Aalto University, Espoo 02150, Finland

WANG Di3,  
  • Affiliation：

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

CHEN Bowei2,  
  • Affiliation：

    Institute of Forest Resource Information Techniques, Chinese Academy of Forestry, Beijing 100091, China

MA Zhenyu2

реферат

The multiple return phenomenon of laser pulse in the technology of full waveform laser scanning has an inevitable impact on the radiometric signal of LiDAR data as well as their radiometric calibration. Previous studies have been made that characterized this phenomenon as an attenuation of laser pulse energy partially intersected by objects, such as canopy and building edges, through the travel path. In this study, we proposed a novel theoretical explanation of multiple returns by establishing a set of LiDAR equations, one for each sub-pulse that composed the original outgoing pulse. The energy attenuation of LiDAR signals through penetrable targets, such as forest canopy, and its influence on radiometric calibration were particularly analyzed. Comparative experiments were conducted with data from one laser instrument of Riegl LMS-Q680i LiDAR system in two different data collection campaigns. One data collection site was covered by LiDAR flight lines of 600 m and 1200 m Above Ground Level (AGL), and the other site with all 600 m AGL. During the data acquisition, ground earth surface with approximate Lambertian reflectivity behavior were measured with filed spectrometer, and the reflectance of ground reference objects were applied in the radiometric calibration process. The data were processed and radiometrically calibrated on the basis of classical LiDAR equation. In addition, multiple return point cloud of a scene with homogeneous ground surface with planted vegetation were extracted for further quantitative analysis. This process was implemented to reveal and characterize the influence of multiple returns on full waveform LiDAR echoes and subsequent target classification. Through the quantitative comparison of data strips, deviations of overlapping data of different flying altitude were calculated. It was demonstrated from the results that the systematic data deviations of LiDAR strip parameters are successfully eliminated. The overall relative errors of corrected diffuse reflectance of the two regions are less than 10% and 5.5%. The standard deviations of strip difference are 0.044 and 0.077 accordingly. Calibration constants in independent LiDAR surveying campaigns are compared. The constants were found to be with correlation to the LiDAR system and flying configurations. Moreover, it was found that LiDAR returns of different return number were not consistent, despite that they were reflected by the same object surface. Significant weakening was observed in the returns of higher orders. It was concluded that multiple return is the major cause of return intensity weakening on homogeneous surfaces and it has crucial effects on radiometric information based target recognition. This problem cannot be readily solved with the current LiDAR observation mechanism in typical mapping scenarios. Challenges from this phenomenon are inevitable to further target recognition and should be addressed for advanced multiple and hyperspectral LiDAR data in the future.

ключеви́че слова́

remote sensing;airborne LiDAR;radiometric calibration;radar equation;uncertainty analysis;classification

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