{"id":"https://openalex.org/W4407638552","doi":"https://doi.org/10.1109/tgrs.2025.3542433","title":"OMRF-HS: Object Markov Random Field With Hierarchical Semantic Regularization for High-Resolution Image Semantic Segmentation","display_name":"OMRF-HS: Object Markov Random Field With Hierarchical Semantic Regularization for High-Resolution Image Semantic Segmentation","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4407638552","doi":"https://doi.org/10.1109/tgrs.2025.3542433"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2025.3542433","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3542433","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-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":null,"display_name":"Haoyu Fu","orcid":"https://orcid.org/0009-0007-1982-1588"},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haoyu Fu","raw_affiliation_strings":["College of Soil and Water Conservation, Southwest Forestry University, Kunming, China"],"raw_orcid":"https://orcid.org/0009-0007-1982-1588","affiliations":[{"raw_affiliation_string":"College of Soil and Water Conservation, Southwest Forestry University, Kunming, China","institution_ids":["https://openalex.org/I25399270"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ruiqi Yang","orcid":"https://orcid.org/0000-0001-6256-5021"},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]},{"id":"https://openalex.org/I4210134523","display_name":"State Forestry and Grassland Administration","ror":"https://ror.org/03f2n3n81","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210134523"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruiqi Yang","raw_affiliation_strings":["College of Big Data and Intelligence Engineering, Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data, Southwest Forestry University, Kunming, China","Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data, College of Big Data and Intelligence Engineering, Southwest Forestry University, Kunming, China"],"raw_orcid":"https://orcid.org/0000-0001-6256-5021","affiliations":[{"raw_affiliation_string":"College of Big Data and Intelligence Engineering, Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data, Southwest Forestry University, Kunming, China","institution_ids":["https://openalex.org/I25399270","https://openalex.org/I4210134523"]},{"raw_affiliation_string":"Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data, College of Big Data and Intelligence Engineering, Southwest Forestry University, Kunming, China","institution_ids":["https://openalex.org/I25399270"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102693077","display_name":"Nan Chen","orcid":"https://orcid.org/0009-0007-2849-788X"},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Nan Chen","raw_affiliation_strings":["College of Soil and Water Conservation, Southwest Forestry University, Kunming, China"],"raw_orcid":"https://orcid.org/0009-0007-2849-788X","affiliations":[{"raw_affiliation_string":"College of Soil and Water Conservation, Southwest Forestry University, Kunming, China","institution_ids":["https://openalex.org/I25399270"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114137440","display_name":"Qinling Dai","orcid":null},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qinling Dai","raw_affiliation_strings":["Art and Design College, Southwest Forestry University, Kunming, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Art and Design College, Southwest Forestry University, Kunming, China","institution_ids":["https://openalex.org/I25399270"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089068361","display_name":"Yili Zhao","orcid":"https://orcid.org/0000-0003-4580-9033"},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]},{"id":"https://openalex.org/I4210134523","display_name":"State Forestry and Grassland Administration","ror":"https://ror.org/03f2n3n81","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210134523"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yili Zhao","raw_affiliation_strings":["College of Big Data and Intelligence Engineering, Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data, Southwest Forestry University, Kunming, China","Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data, College of Big Data and Intelligence Engineering, Southwest Forestry University, Kunming, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Big Data and Intelligence Engineering, Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data, Southwest Forestry University, Kunming, China","institution_ids":["https://openalex.org/I25399270","https://openalex.org/I4210134523"]},{"raw_affiliation_string":"Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data, College of Big Data and Intelligence Engineering, Southwest Forestry University, Kunming, China","institution_ids":["https://openalex.org/I25399270"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009671995","display_name":"Weiheng Xu","orcid":"https://orcid.org/0000-0002-9588-4931"},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]},{"id":"https://openalex.org/I4210134523","display_name":"State Forestry and Grassland Administration","ror":"https://ror.org/03f2n3n81","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210134523"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiheng Xu","raw_affiliation_strings":["College of Big Data and Intelligence Engineering, Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data, Southwest Forestry University, Kunming, China","Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data, College of Big Data and Intelligence Engineering, Southwest Forestry University, Kunming, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Big Data and Intelligence Engineering, Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data, Southwest Forestry University, Kunming, China","institution_ids":["https://openalex.org/I25399270","https://openalex.org/I4210134523"]},{"raw_affiliation_string":"Key Laboratory of National Forestry and Grassland Administration on Forestry and Ecological Big Data, College of Big Data and Intelligence Engineering, Southwest Forestry University, Kunming, China","institution_ids":["https://openalex.org/I25399270"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101506717","display_name":"Guanglong Ou","orcid":"https://orcid.org/0000-0003-1925-6690"},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guanglong Ou","raw_affiliation_strings":["Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming, China","Ministry of Education, Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Southwest Forestry University, Kunming, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Ministry of Education, Southwest Forestry University, Kunming, China","institution_ids":["https://openalex.org/I25399270"]},{"raw_affiliation_string":"Ministry of Education, Key Laboratory for Forest Resources Conservation and Utilization in the Southwest Mountains of China, Southwest Forestry University, Kunming, China","institution_ids":["https://openalex.org/I25399270"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100370666","display_name":"Zheng Chen","orcid":"https://orcid.org/0000-0002-4851-7530"},"institutions":[{"id":"https://openalex.org/I173899330","display_name":"Henan University","ror":"https://ror.org/003xyzq10","country_code":"CN","type":"education","lineage":["https://openalex.org/I173899330"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chen Zheng","raw_affiliation_strings":["School of Mathematics and Statistics, Henan University, Kaifeng, China"],"raw_orcid":"https://orcid.org/0000-0002-4851-7530","affiliations":[{"raw_affiliation_string":"School of Mathematics and Statistics, Henan University, Kaifeng, China","institution_ids":["https://openalex.org/I173899330"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5025539231","display_name":"Leiguang Wang","orcid":"https://orcid.org/0000-0003-2962-1508"},"institutions":[{"id":"https://openalex.org/I25399270","display_name":"Southwest Forestry University","ror":"https://ror.org/03dfa9f06","country_code":"CN","type":"education","lineage":["https://openalex.org/I25399270"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Leiguang Wang","raw_affiliation_strings":["College of Landscape Architecture and Horticulture, Southwest Forestry University, Kunming, China"],"raw_orcid":"https://orcid.org/0000-0003-2962-1508","affiliations":[{"raw_affiliation_string":"College of Landscape Architecture and Horticulture, Southwest Forestry University, Kunming, China","institution_ids":["https://openalex.org/I25399270"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.5884,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.8104443,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":"63","issue":null,"first_page":"1","last_page":"18"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9848999977111816,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9848999977111816,"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/T10052","display_name":"Medical Image Segmentation Techniques","score":0.983299970626831,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9811999797821045,"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":0.7304622530937195},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.659051239490509},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6081812381744385},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5737817287445068},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5235019326210022},{"id":"https://openalex.org/keywords/markov-chain","display_name":"Markov chain","score":0.4733550548553467},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.46155664324760437},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4554353654384613},{"id":"https://openalex.org/keywords/random-field","display_name":"Random field","score":0.45100557804107666},{"id":"https://openalex.org/keywords/markov-process","display_name":"Markov process","score":0.4294452667236328},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.35721534490585327},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18607541918754578},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.14499816298484802},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.1104997992515564}],"concepts":[{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.7304622530937195},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.659051239490509},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6081812381744385},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5737817287445068},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5235019326210022},{"id":"https://openalex.org/C98763669","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov chain","level":2,"score":0.4733550548553467},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.46155664324760437},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4554353654384613},{"id":"https://openalex.org/C130402806","wikidata":"https://www.wikidata.org/wiki/Q5361768","display_name":"Random field","level":2,"score":0.45100557804107666},{"id":"https://openalex.org/C159886148","wikidata":"https://www.wikidata.org/wiki/Q176645","display_name":"Markov process","level":2,"score":0.4294452667236328},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35721534490585327},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18607541918754578},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.14499816298484802},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1104997992515564}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tgrs.2025.3542433","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2025.3542433","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1223319111","display_name":null,"funder_award_id":"32160369","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1689056155","display_name":null,"funder_award_id":"202202AD080010","funder_id":"https://openalex.org/F4320325440","funder_display_name":"Yunnan Provincial Science and Technology Department"},{"id":"https://openalex.org/G1730293736","display_name":null,"funder_award_id":"31860182","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G2336379292","display_name":null,"funder_award_id":"YNWR-QNBJ-2019-026","funder_id":"https://openalex.org/F4320329867","funder_display_name":"Ten Thousand Talent Plans for Young Top-notch Talents of Yunnan Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320325440","display_name":"Yunnan Provincial Science and Technology Department","ror":"https://ror.org/03whqdf70"},{"id":"https://openalex.org/F4320329867","display_name":"Ten Thousand Talent Plans for Young Top-notch Talents of Yunnan Province","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":69,"referenced_works":["https://openalex.org/W318364127","https://openalex.org/W1507028917","https://openalex.org/W2020999234","https://openalex.org/W2050159143","https://openalex.org/W2067191022","https://openalex.org/W2084818612","https://openalex.org/W2146613034","https://openalex.org/W2168175751","https://openalex.org/W2194775991","https://openalex.org/W2529940551","https://openalex.org/W2565639579","https://openalex.org/W2588629605","https://openalex.org/W2607353926","https://openalex.org/W2621331406","https://openalex.org/W2750161770","https://openalex.org/W2766983170","https://openalex.org/W2768420873","https://openalex.org/W2894165434","https://openalex.org/W2908722513","https://openalex.org/W2947739036","https://openalex.org/W2949490564","https://openalex.org/W2963318290","https://openalex.org/W2991488782","https://openalex.org/W2994524071","https://openalex.org/W3033361357","https://openalex.org/W3035406632","https://openalex.org/W3047190427","https://openalex.org/W3100231084","https://openalex.org/W3110908156","https://openalex.org/W3138516171","https://openalex.org/W3159993994","https://openalex.org/W3213181250","https://openalex.org/W4200489582","https://openalex.org/W4206187185","https://openalex.org/W4210408725","https://openalex.org/W4210541032","https://openalex.org/W4214532801","https://openalex.org/W4224882649","https://openalex.org/W4283450732","https://openalex.org/W4288614949","https://openalex.org/W4290712058","https://openalex.org/W4292966455","https://openalex.org/W4298101593","https://openalex.org/W4312628289","https://openalex.org/W4312689172","https://openalex.org/W4313044434","https://openalex.org/W4313117039","https://openalex.org/W4378895202","https://openalex.org/W4380478936","https://openalex.org/W4381885522","https://openalex.org/W4382584061","https://openalex.org/W4384701585","https://openalex.org/W4386634500","https://openalex.org/W4386752954","https://openalex.org/W4386825170","https://openalex.org/W4387205025","https://openalex.org/W4387211147","https://openalex.org/W4387618979","https://openalex.org/W4388152217","https://openalex.org/W4389692500","https://openalex.org/W4389879134","https://openalex.org/W4390872755","https://openalex.org/W4392824739","https://openalex.org/W4392980175","https://openalex.org/W4400579361","https://openalex.org/W4401635340","https://openalex.org/W6847315595","https://openalex.org/W6860142212","https://openalex.org/W6873215316"],"related_works":["https://openalex.org/W4233585817","https://openalex.org/W2016045932","https://openalex.org/W1675950995","https://openalex.org/W4233015508","https://openalex.org/W987019958","https://openalex.org/W2188882668","https://openalex.org/W2078379274","https://openalex.org/W2030382593","https://openalex.org/W1983122994","https://openalex.org/W2088323302"],"abstract_inverted_index":{"As":[0],"spatial":[1,33,127],"resolution":[2],"increases":[3],"in":[4,21,31,41,48],"remote-sensing":[5,50,141],"imagery,":[6],"the":[7,15,57,71,101,117,120,144,154,173,178],"challenge":[8],"of":[9,26,39,56,84,92,103,123,166,177],"semantic":[10,42,46,58,94],"segmentation":[11,47,133,162],"intensifies":[12],"due":[13],"to":[14,17,65],"need":[16],"discern":[18],"intricate":[19],"changes":[20],"terrain.":[22],"Terrain,":[23],"a":[24,36,53],"composite":[25],"diverse":[27],"geographic":[28],"elements":[29],"arranged":[30],"specific":[32],"patterns,":[34],"demands":[35],"higher":[37],"level":[38],"abstraction":[40],"categorization.":[43],"Achieving":[44],"accurate":[45],"high-resolution":[49,140],"images":[51,97,142],"necessitates":[52],"profound":[54],"understanding":[55,102],"structures":[59],"within":[60,96],"complex":[61,104],"scenes.":[62],"In":[63],"response":[64],"this":[66,68,85],"imperative,":[67],"article":[69],"introduces":[70],"object":[72,111],"Markov":[73,112],"random":[74,113],"field":[75,114],"with":[76],"hierarchical":[77],"semantics":[78,125],"(OMRF-HSs)":[79],"method.":[80],"The":[81,164],"primary":[82],"contributions":[83],"work":[86],"are":[87],"twofold:":[88],"1)":[89],"effective":[90],"representation":[91],"layered":[93],"information":[95],"is":[98],"achieved,":[99],"enhancing":[100],"scenes":[105],"and":[106,126,149,168,175],"2)":[107],"unified":[108],"under":[109],"an":[110],"(MRF)":[115],"model,":[116],"method":[118,156],"enables":[119],"joint":[121],"modeling":[122],"structured":[124],"context":[128],"information,":[129],"facilitating":[130],"more":[131],"robust":[132],"outcomes.":[134],"Experimental":[135],"evaluations":[136],"conducted":[137],"on":[138],"multiscene":[139],"from":[143],"aerial":[145],"sensor,":[146],"SPOT5,":[147],"GeoEye,":[148],"Gaofen-2":[150],"satellites":[151],"demonstrate":[152],"that":[153],"proposed":[155],"outperforms":[157],"state-of-the-art":[158],"techniques,":[159],"yielding":[160],"superior":[161],"accuracy.":[163],"availability":[165],"code":[167],"example":[169],"data":[170],"further":[171],"facilitates":[172],"reproducibility":[174],"adoption":[176],"OMRF-HS":[179],"method,":[180],"accessible":[181],"athttps://github.com/FHY-146/OMRF-HS.":[182]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":2}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}