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Correction of Overlapping Multispectral LIDAR Intensity Data: Polynomial Approximation of Range and Angle Effects

Wai Yeung Yan,A. Shaker

Published 2017 in ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

ABSTRACT

Abstract. Recent development of radiometric calibration, correction and normalization approaches have facilitated the use of monochromatic LiDAR intensity and waveform data for land surface analysis and classification. Despite the recent successful attempts, the majority of existing approaches are mainly tailor made for monochromatic LiDAR toward specific land surface scenario. In view of the latest development of multispectral LiDAR sensor, such as the Optech Titan manufactured by Teledyne Optech, a more generic approach should be developed so that the radiometric correction model is able to handle and compensate the laser energy loss with respect to different wavelengths. In this study, we propose a semi-physical approach that aims to utilize high order polynomial functions to model the distortion effects due to the range and the angle. To estimate the parameters of the respect polynomial functions for the range and angle, our approach first locates a pair of closest points within the overlapping LiDAR data strips and subsequently uses a non-linear least squares adjustment to retrieve the polynomial parameters based on the Levenberg-Marquardt algorithm. The approach was tested on a multispectral airborne LiDAR dataset collected by the Optech Titan for the Petawawa Research Forest located in Ontario, Canada. The experimental results demonstrated that the coefficient of variation of the intensity of channel 1 (1550 nm), channel 2 (1064 nm) and channel 3 (532 nm) were reduced by 0.1 % to 39 %, 10 % to 45 % and 12 % to 54 %, respectively. The striping noises, no matter found within single strip and overlapping strips, were significantly reduced after implementing the proposed radiometric correction.

PUBLICATION RECORD

  • Publication year

    2017

  • Venue

    ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences

  • Publication date

    2017-07-25

  • Fields of study

    Computer Science, Engineering, Environmental Science

  • Identifiers
    DOI 10.5194/ISPRS-ARCHIVES-XLII-3-W1-177-2017
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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