An EM algorithm for Lidar deconvolution

Airborne Lidar is a range sensing method which is effective in determining ground terrain from a distance. However, the return signal we observe is a noisy, convolved distortion of the ground return. Deconvolution is one approach to restore the original ground return from the observed return signal. The expectation-maximization (EM) algorithm has been used in signal deconvolution, to produce a maximum-likelihood estimate (MLE) for the original signal. We explain the benefits of the EM algorithm over other benchmark algorithms in Lidar deconvolution, then propose a modified EM algorithm with smoothing and denoising parameters to address some issues with the standard EM algorithm. We then derive a quality metric to test the proposed EM algorithm on simulated and actual data and evaluate its performance. Using our quality metric on simulated data, the proposed algorithm recovers 95% of signal compared to 79% by the benchmark Richardson-Lucy (RL) algorithm, and we show improved image quality and reduced noise on real-life Lidar scenarios.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Operations Research Center

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2021

Author dc:creator

• Yuan, Matthew

Advisors dc:contributor.advisor

• Jaillet, Patrick
• Skelly, Luke

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright retained by author(s)

Licence dc:rights.uri

• https://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/139166

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/139166