Random field models for spatial smoothing of airborne lidar transect data

Amanda R. Coleman; Richard C. Olsen; Herbert K. H. Lee

doi:10.1117/12.2588276

12 April 2021 Random field models for spatial smoothing of airborne lidar transect data

Amanda R. Coleman, Richard C. Olsen, Herbert K. H. Lee

Proceedings Volume 11744, Laser Radar Technology and Applications XXVI; 117440G (2021) https://doi.org/10.1117/12.2588276
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Light Detection and Ranging (LiDAR) is a form of remote sensing that utilizes laser scanners to produce a 3D point cloud of an environment by recording the number of laser pulse returns and measuring the backscattered energy as a function of time. LiDAR transect data were collected over the Monterey Peninsula and the Point Lobos Reserve. An experiment was conducted in the creation of a transect, a very high point density profile, by restricting the scan mirror with the initial goal of better understanding foliage penetration by LiDAR. Because of the high point density of the transect, the data were binned to create synthetic waveforms and to help reduce redundant points. However, the binning introduces sharpness in the data that distorts the typical wave shape in the synthetic transforms. A Bayesian Markov random field model captures the structure in the dataset and helps to offset the sharpness introduced during the binning. After fitting a Markov random field model using Markov chain Monte Carlo, classification methods were applied to distinguish objects in the landscape. These techniques should extend to true waveform data.

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Citation Download Citation

Amanda R. Coleman, Richard C. Olsen, and Herbert K. H. Lee "Random field models for spatial smoothing of airborne lidar transect data", Proc. SPIE 11744, Laser Radar Technology and Applications XXVI, 117440G (12 April 2021); https://doi.org/10.1117/12.2588276

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