{"id":"https://openalex.org/W2166916201","doi":"https://doi.org/10.1109/igarss.2003.1293967","title":"Application of Gaussian Markov random field model to unsupervised classification in polarimetric SAR image","display_name":"Application of Gaussian Markov random field model to unsupervised classification in polarimetric SAR image","publication_year":2004,"publication_date":"2004-05-25","ids":{"openalex":"https://openalex.org/W2166916201","doi":"https://doi.org/10.1109/igarss.2003.1293967","mag":"2166916201"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2003.1293967","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2003.1293967","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477)","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/A5102100023","display_name":"Sahyun Hong","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sahyun Hong","raw_affiliation_strings":["School of Earth & Environmental Sciences, Seoul National University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Earth & Environmental Sciences, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5034120222","display_name":"Wooil M. Moon","orcid":null},"institutions":[{"id":"https://openalex.org/I139264467","display_name":"Seoul National University","ror":"https://ror.org/04h9pn542","country_code":"KR","type":"education","lineage":["https://openalex.org/I139264467"]},{"id":"https://openalex.org/I46247651","display_name":"University of Manitoba","ror":"https://ror.org/02gfys938","country_code":"CA","type":"education","lineage":["https://openalex.org/I46247651"]}],"countries":["CA","KR"],"is_corresponding":false,"raw_author_name":"W.M. Moon","raw_affiliation_strings":["Geophysics, University of Manitoba, Winnipeg, Canada","School of Earth & Environmental Sciences, Seoul National University, Seoul, South Korea"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Geophysics, University of Manitoba, Winnipeg, Canada","institution_ids":["https://openalex.org/I46247651"]},{"raw_affiliation_string":"School of Earth & Environmental Sciences, Seoul National University, Seoul, South Korea","institution_ids":["https://openalex.org/I139264467"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.63140768,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"2","issue":null,"first_page":"929","last_page":"931"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9998000264167786,"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/T11312","display_name":"Soil Moisture and Remote Sensing","score":0.9988999962806702,"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/T11609","display_name":"Geophysical Methods and Applications","score":0.9882000088691711,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6961472034454346},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6542797088623047},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.6076095104217529},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5943742394447327},{"id":"https://openalex.org/keywords/polarimetry","display_name":"Polarimetry","score":0.5508999824523926},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.527317225933075},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4961770474910736},{"id":"https://openalex.org/keywords/random-field","display_name":"Random field","score":0.4913231432437897},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.47145137190818787},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4569855034351349},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.45674648880958557},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.4303879141807556},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3648639917373657},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.19870781898498535},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.17302605509757996},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.12281322479248047},{"id":"https://openalex.org/keywords/scattering","display_name":"Scattering","score":0.10052052140235901},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.09785234928131104}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6961472034454346},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6542797088623047},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.6076095104217529},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5943742394447327},{"id":"https://openalex.org/C28493345","wikidata":"https://www.wikidata.org/wiki/Q899381","display_name":"Polarimetry","level":3,"score":0.5508999824523926},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.527317225933075},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4961770474910736},{"id":"https://openalex.org/C130402806","wikidata":"https://www.wikidata.org/wiki/Q5361768","display_name":"Random field","level":2,"score":0.4913231432437897},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.47145137190818787},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4569855034351349},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.45674648880958557},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.4303879141807556},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3648639917373657},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.19870781898498535},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.17302605509757996},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.12281322479248047},{"id":"https://openalex.org/C191486275","wikidata":"https://www.wikidata.org/wiki/Q210028","display_name":"Scattering","level":2,"score":0.10052052140235901},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.09785234928131104},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2003.1293967","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2003.1293967","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2003. 2003 IEEE International Geoscience and Remote Sensing Symposium. Proceedings (IEEE Cat. No.03CH37477)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320466","display_name":"Gallipoli Medical Research Foundation","ror":"https://ror.org/02p32hv70"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":4,"referenced_works":["https://openalex.org/W2035393454","https://openalex.org/W2111991188","https://openalex.org/W2143057284","https://openalex.org/W2146057518"],"related_works":["https://openalex.org/W987019958","https://openalex.org/W2030382593","https://openalex.org/W1983122994","https://openalex.org/W2078379274","https://openalex.org/W4233015508","https://openalex.org/W2099971354","https://openalex.org/W2133071149","https://openalex.org/W2101965184","https://openalex.org/W2574864235","https://openalex.org/W2166485063"],"abstract_inverted_index":{"The":[0,112,131,189],"aim":[1],"of":[2,32,61,74,94,114,141,154,169,172,204],"this":[3],"paper":[4],"is":[5,151],"to":[6,20,39,58,123,138,181],"demonstrate":[7],"that":[8,119],"the":[9,21,34,56,59,70,104,115,120,139,142,148,160,165,183,200,205,209],"Gaussian":[10,125],"Markov":[11],"random":[12],"field":[13],"(GMRF)":[14],"model":[15,133,150],"can":[16,135],"be":[17,40,124,136],"successfully":[18],"applied":[19,137],"classification":[22,60,102,140,161,191,206],"pf":[23],"multi-frequency":[24],"polarimetric":[25,64,144,195,211],"SAR":[26,65,145,212],"data.":[27,146,213],"As":[28,147],"a":[29,46,152],"special":[30],"case":[31],"MRF,":[33],"GMRF":[35,132,149],"has":[36],"been":[37],"shown":[38],"an":[41],"accurate":[42],"compact":[43],"representation":[44],"from":[45,164],"single-band":[47],"textured":[48,52],"images":[49,93,121],"or":[50],"multi-band":[51],"images.":[53,78],"To":[54],"apply":[55],"method":[57],"inter-channel":[62],"correlated":[63],"data,":[66],"we":[67],"first":[68],"transformed":[69,116,143],"data":[71,98,117],"into":[72],"combination":[73],"uncorrelated":[75],"principal":[76],"component":[77],"Both":[79],"intensities":[80],"(hh,":[81],"hv,":[82],"and":[83,85,96,126,175,202],"vv)":[84],"phase":[86],"difference":[87],"(/spl":[88],"phi//sub":[89],"hh":[90],"-":[91],"vv/)":[92],"L-":[95],"P-band":[97],"are":[99,128,179],"considered":[100],"for":[101,186,208],"in":[103,107,199],"study":[105],"area":[106],"Jeju":[108],"Island,":[109],"South":[110],"Korea.":[111],"properties":[113],"reveal":[118],"tend":[122],"they":[127],"mutually":[129],"uncorrelated.":[130],"therefore":[134],"type":[153],"classifier":[155],"based":[156,193],"on":[157,194],"segment":[158],"merging,":[159],"process":[162],"begins":[163],"initial":[166],"guess":[167],"consisting":[168],"large":[170],"amounts":[171],"segments.":[173],"Spatially":[174],"statistically":[176],"similar":[177],"regions":[178],"combined":[180],"update":[182],"segmented":[184],"map":[185,192],"each":[187],"iteration.":[188],"final":[190],"characteristics":[196],"shows":[197],"improvements":[198],"accuracy":[201],"efficiency":[203],"frame":[207],"tested":[210]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2021,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}