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Reliability Assessment of Gaussian Estimates Derived from Small-footprint Waveform Lidar

Lookup NU author(s): Yu-Ching Lin, Professor Jon MillsORCiD


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The Gaussian decomposition technique has to date played an important role in processing both large- and small-footprint lidar waveforms. When fitting Gaussian functions to received waveforms or the surface response estimated by a deconvolution process, Gaussian coefficients for each detected return can be estimated. These are the temporal position of the Gaussian peak, pulse width and amplitude, which indicate feature characteristics. However, in some circumstance, the estimates may not be fully certain. Little attention has been paid to assessing the reliability or uncertainty of Gaussian estimates. It is necessary to take such indicators into account when application of multiple waveform features is attempted. This study aims to fill this research gap. Whether the reliability of the estimates is affected by the complexity of the waveform shape was investigated. Waveform data collected from simulation experiment and a Riegl LMS-Q560 field campaign was analyzed. Several targets were designed and set in the field to observe how the shape of waveforms varies with the separation distance between two returns and their amplitude magnitude. It was found that the RMSE values for the pulse width estimates based on data simulation were increasingly large when the separation distance was decreased (< 6 ns). The RMSE values for the range estimates based on data simulation remained consistently small (~ 0.04 ns) when decreasing the separation distance.

Publication metadata

Author(s): Lin Y-C, Mills JP, Li C-L

Publication type: Article

Publication status: Published

Journal: Journal of Photogrammetry and Remote Sensing

Year: 2014

Volume: 19

Issue: 2

Pages: 93-106

Print publication date: 01/12/2014

Online publication date: 01/12/2014

Acceptance date: 25/11/2013

Date deposited: 19/12/2017

ISSN (print): 1021-8661

Publisher: Chinese Society Of Photogrammetry & Remote Sensing


DOI: 10.6574/JPRS.2014.19(2).2


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