Classification of terrestrial lidar data directly from digitized echo waveforms

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dc.contributor.authorPashaei, Mohammad
dc.contributor.authorStarek, Michael J.
dc.contributor.authorGlennie, Craig L.
dc.contributor.authorBerryhill, Jacob
dc.creator.orcidhttps://orcid.org/0000-0002-1427-6265en_US
dc.creator.orcidhttps://orcid.org/0000-0002-7996-0594en_US
dc.creator.orcidhttps://orcid.org/0000-0002-1427-6265
dc.creator.orcidhttps://orcid.org/0000-0002-7996-0594
dc.date.accessioned2023-03-08T16:36:29Z
dc.date.available2023-03-08T16:36:29Z
dc.date.issued2023-03-01
dc.description.abstractInformation derived from full-waveform (FW) data collected by FW laser scanning systems has already been shown to be relevant for point cloud analysis tasks. Relevant waveform attributes to populate the corresponding point’s feature vector are typically provided through a post-processing FW analysis (FWA) technique based on fitting the echo waveform with a parametric function describing the shape and location of the echo pulse in the waveform. Samples of the digitized echo are the primary source for any waveform analysis using parametric functions. On the other hand, for some FW laser scanning systems, describing the complex system response model using a simple parametric function seems challenging or impractical. Earlier studies have shown the potential of waveform’s digital samples as relevant waveform attributes, for point cloud classification. The main goal of this study is to extend earlier experiments on direct exploitation of returned waveform signals collected by a FW terrestrial laser scanning (TLS) system in a built environment for point cloud classification, to multi-return waveform signals. Furthermore, the classification performance on feature vectors containing calibrated waveform attributes, derived from a waveform processing approach performed in real-time by the FW TLS system, is evaluated on multiple-echo waveforms and compared with the classification performance derived from the proposed FW data classification technique. Classification performance derived through the proposed technique demonstrates high information content of raw digitized waveform samples. Results show that feature vectors containing samples of digitized echoes carry more information about physical properties of the target than those containing calibrated waveform attributes.en_US
dc.description.abstractInformation derived from full-waveform (FW) data collected by FW laser scanning systems has already been shown to be relevant for point cloud analysis tasks. Relevant waveform attributes to populate the corresponding point’s feature vector are typically provided through a post-processing FW analysis (FWA) technique based on fitting the echo waveform with a parametric function describing the shape and location of the echo pulse in the waveform. Samples of the digitized echo are the primary source for any waveform analysis using parametric functions. On the other hand, for some FW laser scanning systems, describing the complex system response model using a simple parametric function seems challenging or impractical. Earlier studies have shown the potential of waveform’s digital samples as relevant waveform attributes, for point cloud classification. The main goal of this study is to extend earlier experiments on direct exploitation of returned waveform signals collected by a FW terrestrial laser scanning (TLS) system in a built environment for point cloud classification, to multi-return waveform signals. Furthermore, the classification performance on feature vectors containing calibrated waveform attributes, derived from a waveform processing approach performed in real-time by the FW TLS system, is evaluated on multiple-echo waveforms and compared with the classification performance derived from the proposed FW data classification technique. Classification performance derived through the proposed technique demonstrates high information content of raw digitized waveform samples. Results show that feature vectors containing samples of digitized echoes carry more information about physical properties of the target than those containing calibrated waveform attributes.
dc.description.sponsorshipThis work was supported in part by the National Oceanic and Atmospheric Administration (NOAA), U.S. Department of Commerce under Award NA18NOS4000198 and in part by the National Science Foundation (NSF) under Award 2112631.en_US
dc.description.sponsorshipThis work was supported in part by the National Oceanic and Atmospheric Administration (NOAA), U.S. Department of Commerce under Award NA18NOS4000198 and in part by the National Science Foundation (NSF) under Award 2112631.
dc.identifier.citationM. Pashaei, M. J. Starek, C. L. Glennie and J. Berryhill, "Classification of Terrestrial Lidar Data Directly from Digitized Echo Waveforms," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2023.3251187.en_US
dc.identifier.citationM. Pashaei, M. J. Starek, C. L. Glennie and J. Berryhill, "Classification of Terrestrial Lidar Data Directly from Digitized Echo Waveforms," in IEEE Transactions on Geoscience and Remote Sensing, doi: 10.1109/TGRS.2023.3251187.
dc.identifier.doihttps://doi.org/10.1109/TGRS.2023.3251187
dc.identifier.urihttps://hdl.handle.net/1969.6/95585
dc.language.isoen_USen_US
dc.language.isoen_US
dc.rightsAttribution 4.0 International*
dc.rightsAttribution 4.0 International
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/
dc.subjectfull-waveform analysis (FWA)en_US
dc.subjectlidaren_US
dc.subjectterrestrial laser scanning (TLS)en_US
dc.subjectpoint cloud classificationen_US
dc.subjectdeep learningen_US
dc.subjectlaser radaren_US
dc.subjectpoint cloud compressionen_US
dc.subjectshapeen_US
dc.subjectlaser modesen_US
dc.subjectfittingen_US
dc.subjectscatteringen_US
dc.subjectbackscatteren_US
dc.subjectfull-waveform analysis (FWA)
dc.subjectlidar
dc.subjectterrestrial laser scanning (TLS)
dc.subjectpoint cloud classification
dc.subjectdeep learning
dc.subjectlaser radar
dc.subjectpoint cloud compression
dc.subjectshape
dc.subjectlaser modes
dc.subjectfitting
dc.subjectscattering
dc.subjectbackscatter
dc.titleClassification of terrestrial lidar data directly from digitized echo waveformsen_US
dc.titleClassification of terrestrial lidar data directly from digitized echo waveforms
dc.typeArticleen_US
dc.typeArticle

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