{"id":"https://openalex.org/W2135049578","doi":"https://doi.org/10.1109/icip.1996.560918","title":"Extracting line features from synthetic aperture radar (SAR) scenes using a Markov random field model","display_name":"Extracting line features from synthetic aperture radar (SAR) scenes using a Markov random field model","publication_year":2002,"publication_date":"2002-12-24","ids":{"openalex":"https://openalex.org/W2135049578","doi":"https://doi.org/10.1109/icip.1996.560918","mag":"2135049578"},"language":"en","primary_location":{"id":"doi:10.1109/icip.1996.560918","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.1996.560918","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of 3rd IEEE International Conference on Image Processing","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/A5081055268","display_name":"Olaf Hellwich","orcid":"https://orcid.org/0000-0002-2871-9266"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"O. Hellwich","raw_affiliation_strings":["Chair for Photogrammetry and Remote Sensing, Technical University Munich, Munchen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chair for Photogrammetry and Remote Sensing, Technical University Munich, Munchen, Germany","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051753781","display_name":"Helmut Mayer","orcid":"https://orcid.org/0000-0002-9439-2695"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"H. Mayer","raw_affiliation_strings":["Chair for Photogrammetry and Remote Sensing, Technical University Munich, Munchen, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chair for Photogrammetry and Remote Sensing, Technical University Munich, Munchen, Germany","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.20553558,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"3","issue":null,"first_page":"883","last_page":"886"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12549","display_name":"Image and Object Detection Techniques","score":0.9944000244140625,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.9944000244140625,"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/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9933000206947327,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9828000068664551,"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/markov-random-field","display_name":"Markov random field","score":0.7346513867378235},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7106872797012329},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.6571274995803833},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6239039897918701},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.6170845627784729},{"id":"https://openalex.org/keywords/speckle-pattern","display_name":"Speckle pattern","score":0.5898213386535645},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5730674266815186},{"id":"https://openalex.org/keywords/coherence","display_name":"Coherence (philosophical gambling strategy)","score":0.5375245809555054},{"id":"https://openalex.org/keywords/radar-imaging","display_name":"Radar imaging","score":0.5337477922439575},{"id":"https://openalex.org/keywords/speckle-noise","display_name":"Speckle noise","score":0.5126065611839294},{"id":"https://openalex.org/keywords/multiplicative-noise","display_name":"Multiplicative noise","score":0.5016903877258301},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4921981394290924},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.45025834441185},{"id":"https://openalex.org/keywords/line","display_name":"Line (geometry)","score":0.4101901948451996},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.35023361444473267},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.23644015192985535},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.22040730714797974},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.21792420744895935},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09743180871009827},{"id":"https://openalex.org/keywords/transmission","display_name":"Transmission (telecommunications)","score":0.0755767822265625}],"concepts":[{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.7346513867378235},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7106872797012329},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.6571274995803833},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6239039897918701},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.6170845627784729},{"id":"https://openalex.org/C102290492","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle pattern","level":2,"score":0.5898213386535645},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5730674266815186},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.5375245809555054},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.5337477922439575},{"id":"https://openalex.org/C180940675","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle noise","level":3,"score":0.5126065611839294},{"id":"https://openalex.org/C18015164","wikidata":"https://www.wikidata.org/wiki/Q6935000","display_name":"Multiplicative noise","level":5,"score":0.5016903877258301},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4921981394290924},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.45025834441185},{"id":"https://openalex.org/C198352243","wikidata":"https://www.wikidata.org/wiki/Q37105","display_name":"Line (geometry)","level":2,"score":0.4101901948451996},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.35023361444473267},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.23644015192985535},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.22040730714797974},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.21792420744895935},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09743180871009827},{"id":"https://openalex.org/C761482","wikidata":"https://www.wikidata.org/wiki/Q118093","display_name":"Transmission (telecommunications)","level":2,"score":0.0755767822265625},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C131021393","wikidata":"https://www.wikidata.org/wiki/Q7512759","display_name":"Signal transfer function","level":4,"score":0.0},{"id":"https://openalex.org/C13412647","wikidata":"https://www.wikidata.org/wiki/Q174948","display_name":"Analog signal","level":3,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/icip.1996.560918","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.1996.560918","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of 3rd IEEE International Conference on Image Processing","raw_type":"proceedings-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.56.3089","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.56.3089","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.photo.verm.tu-muenchen.de/radar/lausanne.ps.gz","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.550000011920929,"id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W186419298","https://openalex.org/W1566848733","https://openalex.org/W1975347368","https://openalex.org/W2020999234","https://openalex.org/W2038131935","https://openalex.org/W2101897555","https://openalex.org/W2102150301","https://openalex.org/W2109660855","https://openalex.org/W2121026270","https://openalex.org/W2125384310","https://openalex.org/W2142054093","https://openalex.org/W2144494233","https://openalex.org/W2161595292"],"related_works":["https://openalex.org/W2065648684","https://openalex.org/W2009383287","https://openalex.org/W2042914788","https://openalex.org/W2182190754","https://openalex.org/W4321264664","https://openalex.org/W2016481886","https://openalex.org/W2055824452","https://openalex.org/W2727313114","https://openalex.org/W2121688719","https://openalex.org/W2162457349"],"abstract_inverted_index":{"Due":[0],"to":[1,26,53,59],"the":[2,8,55,62,87,105,108],"speckle":[3],"effect":[4],"of":[5,10,48,65,71,107,133],"coherent":[6],"imaging":[7],"detection":[9],"lines":[11,28],"in":[12,20,29,119],"SAR":[13,97,135],"scenes":[14],"is":[15,42,75,90,100,112],"considerably":[16],"move":[17],"difficult":[18],"than":[19],"optical":[21],"images.":[22],"A":[23],"new":[24],"approach":[25,126],"detect":[27],"noisy":[30],"images":[31],"using":[32],"a":[33,82,93,113,120,134],"Markov":[34,56],"random":[35,83],"field":[36],"(MRF)":[37],"model":[38],"and":[39,138],"Bayesian":[40],"classification":[41],"proposed.":[43],"The":[44,69,125],"unobservable":[45],"object":[46,63],"classes":[47,64],"single":[49],"pixels":[50,67,74],"are":[51],"assumed":[52],"fulfil":[54],"condition,":[57],"i.e.":[58],"depend":[60],"on":[61,78],"neighboring":[66,72],"only.":[68],"influence":[70],"line":[73,110],"formulated":[76],"based":[77],"potentials":[79],"derived":[80],"from":[81,130],"walk":[84],"model.":[85],"Locally,":[86],"image":[88],"data":[89,99],"evaluated":[91],"with":[92],"rotating":[94],"template.":[95],"As":[96],"intensity":[98,115],"deteriorated":[101],"by":[102],"multiplicative":[103],"noise,":[104],"response":[106],"local":[109],"detector":[111],"normalized":[114],"ratio":[116],"which":[117],"results":[118],"constant":[121],"false":[122],"alarm":[123],"rate.":[124],"integrates":[127],"intensity,":[128],"coherence":[129],"interferometric":[131],"processing":[132],"scene":[136],"pair,":[137],"given":[139],"Geographic":[140],"Information":[141],"System":[142],"(GIS)":[143],"data.":[144]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-08-26T07:47:46.906454","created_date":"2025-10-10T00:00:00"}