{"id":"https://openalex.org/W4285323947","doi":"https://doi.org/10.1109/ingarss51564.2021.9791926","title":"Hyperspectral Data Assimilation and Road Material Extraction","display_name":"Hyperspectral Data Assimilation and Road Material Extraction","publication_year":2021,"publication_date":"2021-12-06","ids":{"openalex":"https://openalex.org/W4285323947","doi":"https://doi.org/10.1109/ingarss51564.2021.9791926"},"language":"en","primary_location":{"id":"doi:10.1109/ingarss51564.2021.9791926","doi":"https://doi.org/10.1109/ingarss51564.2021.9791926","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ingarss51564.2021.9791926","pdf_url":null,"source":{"id":"https://openalex.org/S7407085884","display_name":"IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2021 IEEE International India Geoscience and Remote Sensing Symposium (InGARSS)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5078773873","display_name":"Ankit Chandelia","orcid":"https://orcid.org/0000-0002-6657-2162","observed_orcids":["https://orcid.org/0000-0002-6657-2162"]},"institutions":[{"id":"https://openalex.org/I165831266","display_name":"Nirma University","ror":"https://ror.org/05qkq7x38","country_code":"IN","type":"education","lineage":["https://openalex.org/I165831266"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Ankit Chandelia","raw_affiliation_strings":["Nirma University,Civil Engineering Department,Ahmedabad,India,382481"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nirma University,Civil Engineering Department,Ahmedabad,India,382481","institution_ids":["https://openalex.org/I165831266"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103010175","display_name":"Parul Patel","orcid":"https://orcid.org/0000-0001-7002-4348","observed_orcids":["https://orcid.org/0000-0001-7002-4348"]},"institutions":[{"id":"https://openalex.org/I165831266","display_name":"Nirma University","ror":"https://ror.org/05qkq7x38","country_code":"IN","type":"education","lineage":["https://openalex.org/I165831266"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Parul R. Patel","raw_affiliation_strings":["Nirma University,Civil Engineering Department,Ahmedabad,India,382481"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nirma University,Civil Engineering Department,Ahmedabad,India,382481","institution_ids":["https://openalex.org/I165831266"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113949320","display_name":"Dhwanilnath Gharekhan","orcid":"https://orcid.org/0000-0002-6699-7494","observed_orcids":["https://orcid.org/0000-0002-6699-7494"]},"institutions":[{"id":"https://openalex.org/I165831266","display_name":"Nirma University","ror":"https://ror.org/05qkq7x38","country_code":"IN","type":"education","lineage":["https://openalex.org/I165831266"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Dhwanilnath Gharekhan","raw_affiliation_strings":["Nirma University,Civil Engineering Department,Ahmedabad,India,382481"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nirma University,Civil Engineering Department,Ahmedabad,India,382481","institution_ids":["https://openalex.org/I165831266"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I165831266"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"508","last_page":"511"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.0007999999797903001,"subfield":{"id":"https://openalex.org/subfields/3305","display_name":"Geography, Planning and Development"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12157","display_name":"Geochemistry and Geologic Mapping","score":9.999999747378752e-05,"subfield":{"id":"https://openalex.org/subfields/1906","display_name":"Geochemistry and Petrology"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/multi-stage-classification","display_name":"multi-stage classification","score":0.8510000109672546},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"support vector machine","score":0.8270000219345093},{"id":"https://openalex.org/keywords/built-up-area-extraction","display_name":"built-up area extraction","score":0.7170000076293945},{"id":"https://openalex.org/keywords/material-identification","display_name":"material identification","score":0.8539999723434448},{"id":"https://openalex.org/keywords/maximum-likelihood-classifier","display_name":"maximum likelihood classification","score":0.8320000171661377},{"id":"https://openalex.org/keywords/minimum-distance-classification","display_name":"minimum distance classification","score":0.9860000014305115},{"id":"https://openalex.org/keywords/mahalanobis-distance-classification","display_name":"Mahalanobis distance classification","score":0.9179999828338623},{"id":"https://openalex.org/keywords/hyperspectral-data-cubes","display_name":"hyperspectral data cubes","score":0.6230000257492065}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8293333053588867},{"id":"https://openalex.org/C1921717","wikidata":"https://www.wikidata.org/wiki/Q1334846","display_name":"Mahalanobis distance","level":2,"score":0.8290711641311646},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.7625539302825928},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6819909811019897},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5929388999938965},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5544155836105347},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5493537783622742},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.47390544414520264},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.46789276599884033},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.46604716777801514},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4581114649772644},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4500312805175781},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10595560073852539},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ingarss51564.2021.9791926","doi":"https://doi.org/10.1109/ingarss51564.2021.9791926","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ingarss51564.2021.9791926","pdf_url":null,"source":{"id":"https://openalex.org/S7407085884","display_name":"IEEE India Geoscience and Remote Sensing Symposium (InGARSS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"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":"2021 IEEE International India Geoscience and Remote Sensing Symposium (InGARSS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5378999710083008,"id":"https://metadata.un.org/sdg/9","display_name":"Industry, innovation and infrastructure"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1656974909","https://openalex.org/W2000779965","https://openalex.org/W2048521685","https://openalex.org/W2150434735","https://openalex.org/W2321228163","https://openalex.org/W2481105521","https://openalex.org/W2752509951"],"related_works":["https://openalex.org/W2765685702","https://openalex.org/W3036723008","https://openalex.org/W7117982458","https://openalex.org/W3173326823","https://openalex.org/W2898849494","https://openalex.org/W2889956472","https://openalex.org/W2041323668","https://openalex.org/W2128769917","https://openalex.org/W2038462111","https://openalex.org/W2103304742"],"abstract_inverted_index":{"Hyperspectral":[0,22],"Remote":[1],"Sensing":[2],"is":[3,60],"a":[4,17,26,74,84,98],"sub-branch":[5],"of":[6,29,34,56,92,123,134],"remote":[7],"sensing,":[8],"which":[9],"captures":[10,25],"information":[11],"in":[12,31,137],"contiguous":[13,36],"spectral":[14],"bands":[15],"over":[16,97],"very":[18],"narrow":[19],"wavelength":[20],"range.":[21],"data":[23],"cube":[24],"large":[27],"number":[28],"datasets":[30,42],"the":[32,41,90,104,131,139],"form":[33],"multiple":[35,44],"bands.":[37],"Data":[38],"Classification":[39],"divides":[40],"into":[43],"classes":[45],"for":[46,62,89],"feature":[47],"extraction,":[48],"material":[49,96],"identification":[50],"and":[51,94,119,150],"temporal":[52],"change":[53],"analysis.":[54],"Selection":[55],"proper":[57],"classification":[58,87,108,125,140,144,154],"technique":[59],"vital":[61],"higher":[63],"accuracy.":[64,79,160],"Single":[65],"stage":[66,143,153],"classifications":[67],"sometimes":[68],"cannot":[69],"extract":[70],"sub":[71],"features":[72],"within":[73],"single":[75],"class":[76],"with":[77],"required":[78],"The":[80,101],"current":[81],"study":[82,99,102],"deploys":[83],"multi":[85],"staged":[86],"approach":[88],"extraction":[91],"built-up":[93],"road":[95],"region.":[100],"compares":[103],"performance":[105],"between":[106],"various":[107],"techniques":[109],"like":[110],"Support":[111,127],"Vector":[112,128],"Machines":[113,129],"(SVM),":[114],"Maximum":[115],"Likelihood,":[116],"Minimum":[117],"Distance":[118],"Mahalanobis":[120],"Distance.":[121],"Out":[122],"all":[124],"techniques,":[126],"provided":[130],"highest":[132],"accuracy":[133,149],"nearly":[135],"90%":[136],"both":[138],"stages.":[141],"First":[142],"using":[145],"SVM":[146],"delivered":[147,158],"89.3%":[148],"further":[151],"2nd":[152],"i.e.,":[155],"Material":[156],"Identification":[157],"90.0%":[159]},"counts_by_year":[],"updated_date":"2026-10-29T09:56:32.521200","created_date":"2022-07-14T00:00:00"}