{"id":"https://openalex.org/W2054133810","doi":"https://doi.org/10.1155/s1110865704409093","title":"Fast Road Network Extraction in Satellite Images Using Mathematical Morphology and Markov Random Fields","display_name":"Fast Road Network Extraction in Satellite Images Using Mathematical Morphology and Markov Random Fields","publication_year":2004,"publication_date":"2004-12-02","ids":{"openalex":"https://openalex.org/W2054133810","doi":"https://doi.org/10.1155/s1110865704409093","mag":"2054133810"},"language":"en","primary_location":{"id":"doi:10.1155/s1110865704409093","is_oa":true,"landing_page_url":"https://doi.org/10.1155/s1110865704409093","pdf_url":null,"source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1155/s1110865704409093","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5068813606","display_name":"Thierry G\u00e9raud","orcid":"https://orcid.org/0000-0002-0380-7948"},"institutions":[{"id":"https://openalex.org/I4210138808","display_name":"\u00c9cole Pour l'Informatique et les Techniques Avanc\u00e9es","ror":"https://ror.org/04r5ddq29","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210138808"]}],"countries":["FR"],"is_corresponding":true,"raw_author_name":"Thierry G\u00e9raud","raw_affiliation_strings":["EPITA Research and Development Laboratory, 14-16 Rue Voltaire, Le Kremlin-Bic\u00eatre Cedex, 94276, France","EPITA Research and Development Laboratory, Le Kremlin-Bic\u00eatre cedex, France#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EPITA Research and Development Laboratory, 14-16 Rue Voltaire, Le Kremlin-Bic\u00eatre Cedex, 94276, France","institution_ids":["https://openalex.org/I4210138808"]},{"raw_affiliation_string":"EPITA Research and Development Laboratory, Le Kremlin-Bic\u00eatre cedex, France#TAB#","institution_ids":["https://openalex.org/I4210138808"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032227198","display_name":"Jean-Baptiste Mouret","orcid":"https://orcid.org/0000-0002-2513-027X"},"institutions":[{"id":"https://openalex.org/I4210138808","display_name":"\u00c9cole Pour l'Informatique et les Techniques Avanc\u00e9es","ror":"https://ror.org/04r5ddq29","country_code":"FR","type":"education","lineage":["https://openalex.org/I4210138808"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Jean-Baptiste Mouret","raw_affiliation_strings":["EPITA Research and Development Laboratory, 14-16 Rue Voltaire, Le Kremlin-Bic\u00eatre Cedex, 94276, France","EPITA Research and Development Laboratory, Le Kremlin-Bic\u00eatre cedex, France#TAB#"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"EPITA Research and Development Laboratory, 14-16 Rue Voltaire, Le Kremlin-Bic\u00eatre Cedex, 94276, France","institution_ids":["https://openalex.org/I4210138808"]},{"raw_affiliation_string":"EPITA Research and Development Laboratory, Le Kremlin-Bic\u00eatre cedex, France#TAB#","institution_ids":["https://openalex.org/I4210138808"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5068813606"],"corresponding_institution_ids":["https://openalex.org/I4210138808"],"apc_list":{"value":1140,"currency":"GBP","value_usd":1398},"apc_paid":{"value":1140,"currency":"GBP","value_usd":1398},"fwci":2.5576,"has_fulltext":true,"cited_by_count":33,"citation_normalized_percentile":{"value":0.88689491,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"2004","issue":"16","first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":1.0,"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"}},"topics":[{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":1.0,"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/T11164","display_name":"Remote Sensing and LiDAR Applications","score":0.9965000152587891,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9789999723434448,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6696292161941528},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.6128856539726257},{"id":"https://openalex.org/keywords/mathematical-morphology","display_name":"Mathematical morphology","score":0.535416305065155},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.5303424596786499},{"id":"https://openalex.org/keywords/adjacency-list","display_name":"Adjacency list","score":0.5136153697967529},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4878675937652588},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4791775941848755},{"id":"https://openalex.org/keywords/adjacency-matrix","display_name":"Adjacency matrix","score":0.47558510303497314},{"id":"https://openalex.org/keywords/image-processing","display_name":"Image processing","score":0.4599427878856659},{"id":"https://openalex.org/keywords/curvilinear-coordinates","display_name":"Curvilinear coordinates","score":0.4411735534667969},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.42942002415657043},{"id":"https://openalex.org/keywords/watershed","display_name":"Watershed","score":0.42927467823028564},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4172738194465637},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.39740777015686035},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3923875391483307},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.39109134674072266},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.3404933214187622},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.24369606375694275},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.23495939373970032},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.1835651695728302},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.10963058471679688},{"id":"https://openalex.org/keywords/geometry","display_name":"Geometry","score":0.09887170791625977}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6696292161941528},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.6128856539726257},{"id":"https://openalex.org/C185568154","wikidata":"https://www.wikidata.org/wiki/Q530242","display_name":"Mathematical morphology","level":4,"score":0.535416305065155},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.5303424596786499},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.5136153697967529},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4878675937652588},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4791775941848755},{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.47558510303497314},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.4599427878856659},{"id":"https://openalex.org/C98343798","wikidata":"https://www.wikidata.org/wiki/Q1790208","display_name":"Curvilinear coordinates","level":2,"score":0.4411735534667969},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.42942002415657043},{"id":"https://openalex.org/C150547873","wikidata":"https://www.wikidata.org/wiki/Q947851","display_name":"Watershed","level":2,"score":0.42927467823028564},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4172738194465637},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.39740777015686035},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3923875391483307},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.39109134674072266},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.3404933214187622},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.24369606375694275},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.23495939373970032},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.1835651695728302},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.10963058471679688},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.09887170791625977}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1155/s1110865704409093","is_oa":true,"landing_page_url":"https://doi.org/10.1155/s1110865704409093","pdf_url":null,"source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.712.5843","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.712.5843","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"https://www.lrde.epita.fr/%7Etheo/papers/geraud.2003.nsip.pdf","raw_type":"text"},{"id":"pmh:oai:doaj.org/article:44077f2624814b76a7a10fa027959ffd","is_oa":true,"landing_page_url":"https://doaj.org/article/44077f2624814b76a7a10fa027959ffd","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","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":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"EURASIP Journal on Advances in Signal Processing, Vol 2004, Iss 16, p 473593 (2004)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1155/s1110865704409093","is_oa":true,"landing_page_url":"https://doi.org/10.1155/s1110865704409093","pdf_url":null,"source":{"id":"https://openalex.org/S35920007","display_name":"EURASIP Journal on Advances in Signal Processing","issn_l":"1687-6172","issn":["1687-6172","1687-6180"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"EURASIP Journal on Advances in Signal Processing","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":32,"referenced_works":["https://openalex.org/W161217653","https://openalex.org/W1484720787","https://openalex.org/W1509834943","https://openalex.org/W1511279056","https://openalex.org/W1543237031","https://openalex.org/W1545790611","https://openalex.org/W1555212262","https://openalex.org/W1562474711","https://openalex.org/W1566450176","https://openalex.org/W1569826416","https://openalex.org/W1587905605","https://openalex.org/W1987365705","https://openalex.org/W2010792042","https://openalex.org/W2020999234","https://openalex.org/W2024060531","https://openalex.org/W2054133810","https://openalex.org/W2086619084","https://openalex.org/W2098152234","https://openalex.org/W2124260943","https://openalex.org/W2127731579","https://openalex.org/W2128356031","https://openalex.org/W2128439793","https://openalex.org/W2129872026","https://openalex.org/W2134188917","https://openalex.org/W2144572173","https://openalex.org/W2156155065","https://openalex.org/W2168990496","https://openalex.org/W2170749316","https://openalex.org/W4233497883","https://openalex.org/W4241385190","https://openalex.org/W6656506093","https://openalex.org/W6682791742"],"related_works":["https://openalex.org/W4213150077","https://openalex.org/W2369410163","https://openalex.org/W2059018062","https://openalex.org/W2604585036","https://openalex.org/W2078477160","https://openalex.org/W1989103179","https://openalex.org/W1991172810","https://openalex.org/W125803343","https://openalex.org/W2153421018","https://openalex.org/W2117632582"],"abstract_inverted_index":{"We":[0],"present":[1],"a":[2,17,57,63,73,95,114,121,124,136,159,168,202],"fast":[3],"method":[4,89,208],"for":[5],"road":[6,38,74,84,109,165,191,198],"network":[7,85,199],"extraction":[8,33,200],"in":[9,49,132],"satellite":[10],"images.":[11],"It":[12],"can":[13,46,60,104,209],"be":[14,47,105,130,211],"seen":[15],"as":[16,201],"transposition":[18],"of":[19,34,43,126,135,190,197,221],"the":[20,32,50,83,88,117,133,143,148,154,164,195,219],"segmentation":[21],"scheme":[22],"\"watershed":[23],"transform":[24,156],"region":[25],"adjacency":[26,171],"graph":[27,169],"Markov":[28,179],"random":[29,180],"fields\"":[30],"to":[31,92,120,129,157,194,213],"curvilinear":[35,222],"objects.":[36],"Many":[37],"extractors":[39],"which":[40,162],"are":[41],"composed":[42],"two":[44],"stages":[45],"found":[48],"literature.":[51],"The":[52,77],"first":[53],"one":[54],"acts":[55],"like":[56],"filter":[58],"that":[59,98,103],"decide":[61],"from":[62,107],"local":[64],"analysis,":[65],"at":[66,81],"every":[67],"image":[68,101,145,215],"point,":[69],"if":[70],"there":[71],"is":[72,123,176,224],"or":[75],"not.":[76],"second":[78],"stage":[79],"aims":[80],"obtaining":[82],"structure.":[86],"In":[87,112],"we":[90],"propose":[91],"rely":[93],"on":[94,142,147],"\"potential\"":[96],"image,":[97,116],"is,":[99],"unstructured":[100],"data":[102],"derived":[106],"any":[108],"extractor":[110],"filter.":[111],"such":[113],"potential":[115,144],"value":[118],"assigned":[119],"point":[122],"measure":[125],"its":[127],"likelihood":[128],"located":[131],"middle":[134],"road.":[137],"A":[138],"filtering":[139],"step":[140],"applied":[141],"relies":[146],"area":[149],"closing":[150],"operator":[151],"followed":[152],"by":[153],"watershed":[155,174],"obtain":[158],"connected":[160],"line":[161],"encloses":[163],"network.":[166],"Then":[167],"describing":[170],"relationships":[172],"between":[173],"lines":[175],"built.":[177],"Defining":[178],"fields":[181],"upon":[182],"this":[183],"graph,":[184],"associated":[185],"with":[186],"an":[187],"energetic":[188],"model":[189],"networks,":[192],"leads":[193],"expression":[196],"global":[203],"energy":[204],"minimization":[205],"problem.":[206],"This":[207],"easily":[210],"adapted":[212],"other":[214],"processing":[216],"fields,":[217],"where":[218],"recognition":[220],"structures":[223],"involved.":[225]},"counts_by_year":[{"year":2024,"cited_by_count":3},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1},{"year":2013,"cited_by_count":2},{"year":2012,"cited_by_count":4}],"updated_date":"2026-06-19T15:47:20.252518","created_date":"2025-10-10T00:00:00"}
