{"id":"https://openalex.org/W4312650119","doi":"https://doi.org/10.1109/igarss46834.2022.9883782","title":"Full-Waveform Terrestrial Lidar Data Classification Using Raw Digitized Waveform Signals","display_name":"Full-Waveform Terrestrial Lidar Data Classification Using Raw Digitized Waveform Signals","publication_year":2022,"publication_date":"2022-07-17","ids":{"openalex":"https://openalex.org/W4312650119","doi":"https://doi.org/10.1109/igarss46834.2022.9883782"},"language":"en","primary_location":{"id":"doi:10.1109/igarss46834.2022.9883782","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9883782","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5042931415","display_name":"Mohammad Pashaei","orcid":"https://orcid.org/0000-0002-1427-6265"},"institutions":[{"id":"https://openalex.org/I96749437","display_name":"Texas A&M University \u2013 Corpus Christi","ror":"https://ror.org/01mrfdz82","country_code":"US","type":"education","lineage":["https://openalex.org/I96749437"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mohammad Pashaei","raw_affiliation_strings":["Conrad Blucher Institute for Surveying and Science, Texas A&M University-Corpus Christi,Texas,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Conrad Blucher Institute for Surveying and Science, Texas A&M University-Corpus Christi,Texas,USA","institution_ids":["https://openalex.org/I96749437"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062945695","display_name":"Michael J. Starek","orcid":"https://orcid.org/0000-0002-7996-0594"},"institutions":[{"id":"https://openalex.org/I96749437","display_name":"Texas A&M University \u2013 Corpus Christi","ror":"https://ror.org/01mrfdz82","country_code":"US","type":"education","lineage":["https://openalex.org/I96749437"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Michael J. Starek","raw_affiliation_strings":["Conrad Blucher Institute for Surveying and Science, Texas A&M University-Corpus Christi,Texas,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Conrad Blucher Institute for Surveying and Science, Texas A&M University-Corpus Christi,Texas,USA","institution_ids":["https://openalex.org/I96749437"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021596051","display_name":"Jacob Berryhill","orcid":null},"institutions":[{"id":"https://openalex.org/I96749437","display_name":"Texas A&M University \u2013 Corpus Christi","ror":"https://ror.org/01mrfdz82","country_code":"US","type":"education","lineage":["https://openalex.org/I96749437"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jacob Berryhill","raw_affiliation_strings":["Conrad Blucher Institute for Surveying and Science, Texas A&M University-Corpus Christi,Texas,USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Conrad Blucher Institute for Surveying and Science, Texas A&M University-Corpus Christi,Texas,USA","institution_ids":["https://openalex.org/I96749437"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I96749437"],"apc_list":null,"apc_paid":null,"fwci":0.6399,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.62978953,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1916","last_page":"1919"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11164","display_name":"Remote Sensing and LiDAR Applications","score":1.0,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10111","display_name":"Remote Sensing in Agriculture","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12153","display_name":"Advanced Optical Sensing Technologies","score":0.9933000206947327,"subfield":{"id":"https://openalex.org/subfields/3105","display_name":"Instrumentation"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/waveform","display_name":"Waveform","score":0.9144567251205444},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.7507687211036682},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6686761379241943},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5785488486289978},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5658336877822876},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5601438283920288},{"id":"https://openalex.org/keywords/point-cloud","display_name":"Point cloud","score":0.5593628883361816},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5346391201019287},{"id":"https://openalex.org/keywords/ranging","display_name":"Ranging","score":0.5099629163742065},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45175400376319885},{"id":"https://openalex.org/keywords/parametric-statistics","display_name":"Parametric statistics","score":0.44042930006980896},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.41522857546806335},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1525474488735199},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.13998398184776306},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11319643259048462},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.0838199257850647}],"concepts":[{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.9144567251205444},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.7507687211036682},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6686761379241943},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5785488486289978},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5658336877822876},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5601438283920288},{"id":"https://openalex.org/C131979681","wikidata":"https://www.wikidata.org/wiki/Q1899648","display_name":"Point cloud","level":2,"score":0.5593628883361816},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5346391201019287},{"id":"https://openalex.org/C115051666","wikidata":"https://www.wikidata.org/wiki/Q6522493","display_name":"Ranging","level":2,"score":0.5099629163742065},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45175400376319885},{"id":"https://openalex.org/C117251300","wikidata":"https://www.wikidata.org/wiki/Q1849855","display_name":"Parametric statistics","level":2,"score":0.44042930006980896},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.41522857546806335},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1525474488735199},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.13998398184776306},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11319643259048462},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0838199257850647},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss46834.2022.9883782","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss46834.2022.9883782","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.5400000214576721,"display_name":"Reduced inequalities"}],"awards":[{"id":"https://openalex.org/G4317428490","display_name":null,"funder_award_id":"NA18NOS4000198","funder_id":"https://openalex.org/F4320310164","funder_display_name":"Texas A and M University"}],"funders":[{"id":"https://openalex.org/F4320310164","display_name":"Texas A and M University","ror":"https://ror.org/01f5ytq51"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1967416652","https://openalex.org/W2011287807","https://openalex.org/W2037282532","https://openalex.org/W2086480514","https://openalex.org/W2131256165","https://openalex.org/W4206023917","https://openalex.org/W6806676949"],"related_works":["https://openalex.org/W2783354812","https://openalex.org/W4384112194","https://openalex.org/W2103009189","https://openalex.org/W4312958259","https://openalex.org/W4308259661","https://openalex.org/W4390813131","https://openalex.org/W2594043982","https://openalex.org/W3036493597","https://openalex.org/W4293094720","https://openalex.org/W2739701376"],"abstract_inverted_index":{"Full-waveform":[0],"(FW)":[1,105],"data":[2,89],"collected":[3,91],"by":[4],"FW":[5],"light":[6],"detection":[7],"and":[8,99],"ranging":[9],"(lidar)":[10],"system":[11],"have":[12],"long":[13],"been":[14,27],"used":[15],"to":[16,131],"enhance":[17],"the":[18,21,32,35,39,46,58,72,82,123],"characterization":[19],"of":[20,34,42,48,75,84,115,126],"surveyed":[22],"area.":[23],"It":[24],"has":[25],"already":[26],"shown":[28],"that":[29,113],"attributes":[30],"describing":[31],"shape":[33],"echo":[36,59],"sig-nal":[37],"in":[38,136],"feature":[40,70,73],"vector":[41,74],"lidar":[43],"points":[44],"improves":[45],"performance":[47,135],"land":[49],"cover":[50],"classification.":[51],"In":[52],"this":[53],"experiment,":[54],"rather":[55],"than":[56],"fitting":[57],"waveform":[60,118,146],"with":[61,81],"an":[62],"appropriate":[63],"parametric":[64],"model,":[65],"typically":[66],"a":[67,93,103,132,137,152],"Gaussian,":[68],"for":[69],"extraction,":[71],"each":[76],"illuminated":[77,127],"target":[78],"is":[79,90],"populated":[80],"sam-ples":[83],"corresponding":[85],"digitized":[86,117],"waveform.":[87],"Waveform":[88],"over":[92],"built":[94,100],"environment":[95],"including":[96],"both":[97],"natural":[98],"objects":[101],"using":[102],"full-waveform":[104],"terrestrial":[106],"laser":[107],"scanning":[108],"(TLS)":[109],"system.":[110],"Results":[111],"show":[112],"samples":[114],"raw":[116],"carry":[119],"useful":[120],"information":[121],"about":[122],"physi-cal":[124],"properties":[125],"targets":[128],"which":[129],"lead":[130],"relatively":[133],"high":[134],"multi":[138],"-class":[139],"TLS":[140],"point":[141],"cloud":[142],"classifi-cation,":[143],"where":[144],"discriminative":[145],"features":[147],"are":[148],"extracted":[149],"automatically":[150],"within":[151],"proposed":[153],"convolutional":[154],"neural":[155],"network":[156],"(CNN)":[157],"model.":[158]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}