{"id":"https://openalex.org/W1990311762","doi":"https://doi.org/10.1109/igarss.2013.6723772","title":"Urban built-up area extraction using combined spectral information and multivariate texture","display_name":"Urban built-up area extraction using combined spectral information and multivariate texture","publication_year":2013,"publication_date":"2013-07-01","ids":{"openalex":"https://openalex.org/W1990311762","doi":"https://doi.org/10.1109/igarss.2013.6723772","mag":"1990311762"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2013.6723772","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2013.6723772","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS","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/A5100433042","display_name":"Jun Zhang","orcid":"https://orcid.org/0000-0001-5579-7094"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jun Zhang","raw_affiliation_strings":["Institute of Remote Sensing and GIS, Peking University, Beijing, China","Inst. of remote sensing & GIS, Peking Univ., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Remote Sensing and GIS, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Inst. of remote sensing & GIS, Peking Univ., Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100742536","display_name":"Peijun Li","orcid":"https://orcid.org/0000-0002-4989-9892"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peijun Li","raw_affiliation_strings":["Institute of Remote Sensing and GIS, Peking University, Beijing, China","Inst. of remote sensing & GIS, Peking Univ., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Remote Sensing and GIS, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Inst. of remote sensing & GIS, Peking Univ., Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067232074","display_name":"Haiqing Xu","orcid":"https://orcid.org/0000-0003-2084-3278"},"institutions":[{"id":"https://openalex.org/I20231570","display_name":"Peking University","ror":"https://ror.org/02v51f717","country_code":"CN","type":"education","lineage":["https://openalex.org/I20231570"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haiqing Xu","raw_affiliation_strings":["Institute of Remote Sensing and GIS, Peking University, Beijing, China","Inst. of remote sensing & GIS, Peking Univ., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Remote Sensing and GIS, Peking University, Beijing, China","institution_ids":["https://openalex.org/I20231570"]},{"raw_affiliation_string":"Inst. of remote sensing & GIS, Peking Univ., Beijing, China","institution_ids":["https://openalex.org/I20231570"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I20231570"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":{"value":0.11271612,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"4249","last_page":"4252"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10226","display_name":"Land Use and Ecosystem Services","score":0.9945999979972839,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.639197587966919},{"id":"https://openalex.org/keywords/multivariate-statistics","display_name":"Multivariate statistics","score":0.595404326915741},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.5340337753295898},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5091206431388855},{"id":"https://openalex.org/keywords/variogram","display_name":"Variogram","score":0.497226744890213},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.46284931898117065},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4557880461215973},{"id":"https://openalex.org/keywords/information-extraction","display_name":"Information extraction","score":0.45525750517845154},{"id":"https://openalex.org/keywords/built-up-area","display_name":"Built-up area","score":0.44228479266166687},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.424776166677475},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4197104275226593},{"id":"https://openalex.org/keywords/kriging","display_name":"Kriging","score":0.27237504720687866},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.2572672665119171},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.23747730255126953},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.11837181448936462}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.639197587966919},{"id":"https://openalex.org/C161584116","wikidata":"https://www.wikidata.org/wiki/Q1952580","display_name":"Multivariate statistics","level":2,"score":0.595404326915741},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.5340337753295898},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5091206431388855},{"id":"https://openalex.org/C154881674","wikidata":"https://www.wikidata.org/wiki/Q2269270","display_name":"Variogram","level":3,"score":0.497226744890213},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.46284931898117065},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4557880461215973},{"id":"https://openalex.org/C195807954","wikidata":"https://www.wikidata.org/wiki/Q1662562","display_name":"Information extraction","level":2,"score":0.45525750517845154},{"id":"https://openalex.org/C2780105985","wikidata":"https://www.wikidata.org/wiki/Q702492","display_name":"Built-up area","level":3,"score":0.44228479266166687},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.424776166677475},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4197104275226593},{"id":"https://openalex.org/C81692654","wikidata":"https://www.wikidata.org/wiki/Q225926","display_name":"Kriging","level":2,"score":0.27237504720687866},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.2572672665119171},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.23747730255126953},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.11837181448936462},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2013.6723772","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2013.6723772","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE International Geoscience and Remote Sensing Symposium - IGARSS","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.8500000238418579}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1963832478","https://openalex.org/W2040535921","https://openalex.org/W2058172823","https://openalex.org/W2093796724","https://openalex.org/W2124294823","https://openalex.org/W2131438174","https://openalex.org/W2139017994"],"related_works":["https://openalex.org/W2031428567","https://openalex.org/W3144075325","https://openalex.org/W2374611508","https://openalex.org/W3192692000","https://openalex.org/W4205739195","https://openalex.org/W4289096177","https://openalex.org/W2380526267","https://openalex.org/W2134633420","https://openalex.org/W4255440089","https://openalex.org/W2188839588"],"abstract_inverted_index":{"Urban":[0,15],"built-up":[1,16,43,102],"area":[2,17,103,140],"information":[3,57],"is":[4,51,70,85,119],"required":[5],"by":[6],"many":[7],"applications,":[8],"such":[9,31],"as":[10,32,76,79],"research":[11],"of":[12,37,53,125,138],"urbanization":[13],"rate.":[14],"extraction":[18],"using":[19],"moderate":[20],"resolution":[21],"remotely":[22],"sensed":[23],"data":[24,124],"(e.g.":[25],"Landsat":[26,122],"TM/ETM+)":[27],"presents":[28],"numerous":[29],"challenges,":[30],"very":[33],"heterogeneous":[34],"spectral":[35,40],"features":[36],"urban":[38,60,139],"areas,":[39],"confusion":[41],"between":[42],"class":[44],"and":[45,92],"others.":[46],"Considering":[47],"that":[48,130],"image":[49],"texture":[50,84,94],"one":[52],"the":[54,77,131,136],"important":[55],"spatial":[56],"for":[58,101],"identifying":[59],"land":[61],"cover,":[62],"a":[63,80],"new":[64],"methodology":[65],"to":[66],"address":[67],"these":[68],"issues":[69],"proposed.":[71],"This":[72],"approach":[73],"involves":[74],"processes":[75],"following,":[78],"first":[81],"step,":[82],"multivariate":[83,88,93],"computed":[86],"through":[87],"variogram.":[89],"Spectral":[90],"bands":[91],"are":[95],"then":[96],"combined":[97],"in":[98,113],"classification":[99],"process":[100],"extraction.":[104,141],"One-Class":[105],"Support":[106],"Vector":[107],"Machine":[108],"(OCSVM)":[109],"classifier":[110],"was":[111],"used":[112],"this":[114],"process.":[115],"A":[116],"comprehensive":[117],"evaluation":[118],"present":[120],"with":[121],"TM":[123],"Beijing,":[126],"China.":[127],"Results":[128],"demonstrate":[129],"proposed":[132],"method":[133],"significantly":[134],"improves":[135],"accuracy":[137]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":2}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}