{"id":"https://openalex.org/W2167212773","doi":"https://doi.org/10.1109/isda.2009.109","title":"A Comparative Study of Clustering Methods for Urban Areas Segmentation from High Resolution Remote Sensing Image","display_name":"A Comparative Study of Clustering Methods for Urban Areas Segmentation from High Resolution Remote Sensing Image","publication_year":2009,"publication_date":"2009-01-01","ids":{"openalex":"https://openalex.org/W2167212773","doi":"https://doi.org/10.1109/isda.2009.109","mag":"2167212773"},"language":"en","primary_location":{"id":"doi:10.1109/isda.2009.109","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isda.2009.109","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","raw_type":"proceedings-article"},"type":"conference-paper","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/A5022916681","display_name":"Safaa M. Bedawi","orcid":null},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Safaa M. Bedawi","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Waterloo, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039318050","display_name":"Mohamed S. Kamel","orcid":"https://orcid.org/0000-0001-6173-8082"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Mohamed S. Kamel","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Waterloo, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I151746483"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998000264167786,"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":0.9998000264167786,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9970999956130981,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.8640904426574707},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6958041191101074},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.6928295493125916},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6877245903015137},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6361635327339172},{"id":"https://openalex.org/keywords/segmentation-based-object-categorization","display_name":"Segmentation-based object categorization","score":0.5579628944396973},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5080924034118652},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.5048040151596069},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.47201961278915405},{"id":"https://openalex.org/keywords/region-growing","display_name":"Region growing","score":0.47073400020599365},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.45814698934555054},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.41318878531455994},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.2172144055366516}],"concepts":[{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.8640904426574707},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6958041191101074},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.6928295493125916},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6877245903015137},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6361635327339172},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.5579628944396973},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5080924034118652},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.5048040151596069},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.47201961278915405},{"id":"https://openalex.org/C206824153","wikidata":"https://www.wikidata.org/wiki/Q1169834","display_name":"Region growing","level":5,"score":0.47073400020599365},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45814698934555054},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.41318878531455994},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.2172144055366516}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/isda.2009.109","is_oa":false,"landing_page_url":"https://doi.org/10.1109/isda.2009.109","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 Ninth International Conference on Intelligent Systems Design and Applications","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":0.8399999737739563}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1533230241","https://openalex.org/W1564419782","https://openalex.org/W1979432452","https://openalex.org/W2067191022","https://openalex.org/W2070412788","https://openalex.org/W2099184708","https://openalex.org/W2103192805","https://openalex.org/W2116810533","https://openalex.org/W2121947440","https://openalex.org/W2123038009","https://openalex.org/W2126351937","https://openalex.org/W2148878936","https://openalex.org/W2165232124"],"related_works":["https://openalex.org/W3144569342","https://openalex.org/W2394279717","https://openalex.org/W2364730859","https://openalex.org/W2551987074","https://openalex.org/W2386644571","https://openalex.org/W2185902295","https://openalex.org/W2055202857","https://openalex.org/W2103507220","https://openalex.org/W2372421320","https://openalex.org/W2945274617"],"abstract_inverted_index":{"This":[0],"paper":[1,84],"focuses":[2],"on":[3,45,100],"evaluating":[4],"and":[5,58,171],"comparing":[6],"a":[7,41,66],"number":[8],"of":[9,17,30,43,96,102,132,142,153],"clustering":[10,73,88,112,133,154],"methods":[11,89],"used":[12,64,127],"in":[13,27,105,120,174],"color":[14],"image":[15,50,56],"segmentation":[16,51,67,95,101],"high":[18,106,155],"resolution":[19,107,156],"remote":[20,31,108],"sensing":[21,32,109],"images.":[22,97,110],"Despite":[23],"the":[24,28,35,55,130,139,151,161],"enormous":[25],"progress":[26],"analysis":[29],"imagery":[33],"over":[34],"past":[36],"three":[37,162],"decades,":[38],"there":[39],"is":[40,75,145],"lack":[42],"guidance":[44,170],"how":[46],"to":[47,78,117,128],"select":[48],"an":[49,71],"method":[52,74],"suitable":[53],"for":[54,94],"type":[57],"size.":[59],"Clustering":[60],"has":[61],"been":[62,92],"widely":[63],"as":[65],"approach":[68],"therefore,":[69],"choosing":[70],"appropriate":[72],"very":[76],"critical":[77],"achieve":[79],"better":[80],"results.":[81],"In":[82],"this":[83],"we":[85],"compare":[86],"five":[87],"that":[90,138],"have":[91],"suggested":[93],"We":[98],"focus":[99],"urban":[103,121],"areas":[104],"Effective":[111],"extracts":[113],"regions":[114],"which":[115],"correspond":[116],"land":[118],"uses":[119],"areas.":[122],"Ground":[123],"truth":[124],"images":[125,158],"are":[126],"evaluate":[129],"performance":[131],"methods.":[134],"The":[135,148,166],"comparison":[136,167],"shows":[137],"average":[140],"accuracy":[141],"road":[143],"extraction":[144],"above":[146],"75%.":[147],"results":[149],"show":[150],"potential":[152],"aerial":[157],"starting":[159],"from":[160],"RGB":[163],"bands":[164],"only.":[165],"gives":[168],"some":[169],"tradeoffs":[172],"involved":[173],"using":[175],"each.":[176]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}