{"id":{"repo_id":"mit","oai_identifier":"oai:dspace.mit.edu:1721.1/139166"},"canonical_url":"https://search.dev.ndltd.org/etd/mit/oai:dspace.mit.edu:1721.1/139166","repository":{"repo_id":"mit","name":"MIT","base_url":"https://dspace.mit.edu/oai/request"},"display":{"title":"An EM algorithm for Lidar deconvolution","abstract":"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.","abstract_html":"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.","abstract_has_math":false,"creators":["Yuan, Matthew"],"institution":"Massachusetts Institute of Technology","degree_name":"Master","degree_level":null,"degree_discipline":null,"degree_department":"Massachusetts Institute of Technology. Operations Research Center","school":null,"contributors":[],"advisors":["Jaillet, Patrick","Skelly, Luke"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-06","date_published":"2021-06","updated_at":"2026-07-22T22:21:05Z","subjects":[],"languages":[],"rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"rights_urls":["https://rightsstatements.org/page/InC-EDU/1.0/"],"identifier_entries":[]},"links":{"outbound_url":"https://hdl.handle.net/1721.1/139166","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Jaillet, Patrick","Skelly, Luke"]},{"key":"dc:contributor.department","label":"Department","values":["Massachusetts Institute of Technology. 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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."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["S.M."]},{"key":"dc:title","label":"Title","values":["An EM algorithm for Lidar deconvolution"]}]}],"canonical_facts":{"dc:contributor.advisor":["Jaillet, Patrick","Skelly, Luke"],"dc:contributor.department":["Massachusetts Institute of Technology. Operations Research Center"],"dc:creator":["Yuan, Matthew"],"dc:date.accessioned":["2022-01-14T14:54:08Z"],"dc:date.available":["2022-01-14T14:54:08Z"],"dc:date.issued":["2021-06"],"dc:description.abstract":["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."],"dc:description.degree":["S.M."],"dc:identifier.uri":["https://hdl.handle.net/1721.1/139166"],"dc:publisher":["Massachusetts Institute of Technology"],"dc:rights":["In Copyright - Educational Use Permitted","Copyright retained by author(s)"],"dc:rights.uri":["https://rightsstatements.org/page/InC-EDU/1.0/"],"dc:title":["An EM algorithm for Lidar deconvolution"],"dc:type":["Thesis"],"thesis:degree_name":["Master","Master of Science in Operations Research"]},"updated_at":"2026-07-22T22:21:05Z"}