{"id":"https://openalex.org/W4416728310","doi":"https://doi.org/10.1109/igarss55030.2025.11243958","title":"Cross-Scale Water Body Extraction from Very-High-Resolution Remote Sensing Images Based on Segment Anything Model 2","display_name":"Cross-Scale Water Body Extraction from Very-High-Resolution Remote Sensing Images Based on Segment Anything Model 2","publication_year":2025,"publication_date":"2025-08-03","ids":{"openalex":"https://openalex.org/W4416728310","doi":"https://doi.org/10.1109/igarss55030.2025.11243958"},"language":"en","primary_location":{"id":"doi:10.1109/igarss55030.2025.11243958","doi":"https://doi.org/10.1109/igarss55030.2025.11243958","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss55030.2025.11243958","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2025 - 2025 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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":"Tianyi Zhang","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianyi Zhang","raw_affiliation_strings":["Xidian University,School of Artificial Intelligence,Xi&#x2019;an,China,710071"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,School of Artificial Intelligence,Xi&#x2019;an,China,710071","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108982877","display_name":"Yi Ren","orcid":"https://orcid.org/0009-0008-8453-9447","observed_orcids":["https://orcid.org/0009-0008-8453-9447"]},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Ren","raw_affiliation_strings":["Hangzhou Institute of Technology of Xidian University,Lab. of AI,Hangzhou,China,311231"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Hangzhou Institute of Technology of Xidian University,Lab. of AI,Hangzhou,China,311231","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5008943930","display_name":"Weibin Li","orcid":"https://orcid.org/0000-0003-0047-8955","observed_orcids":["https://orcid.org/0000-0003-0047-8955"]},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weibin Li","raw_affiliation_strings":["Xidian University,School of Artificial Intelligence,Xi&#x2019;an,China,710071"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,School of Artificial Intelligence,Xi&#x2019;an,China,710071","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xuan Sun","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xuan Sun","raw_affiliation_strings":["Xidian University,School of Artificial Intelligence,Xi&#x2019;an,China,710071"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,School of Artificial Intelligence,Xi&#x2019;an,China,710071","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5042622072","display_name":"Xihui Feng","orcid":"https://orcid.org/0009-0002-8120-5595","observed_orcids":["https://orcid.org/0009-0002-8120-5595"]},"institutions":[{"id":"https://openalex.org/I211433327","display_name":"Ministry of Natural Resources","ror":"https://ror.org/02kxqx159","country_code":"CN","type":"government","lineage":["https://openalex.org/I211433327","https://openalex.org/I4210127390"]},{"id":"https://openalex.org/I4405273276","display_name":"Key Laboratory of Coal Resources Exploration and Comprehensive Utilization, Ministry of Land and Resources","ror":"https://ror.org/05kcmsd90","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4405273276"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xihui Feng","raw_affiliation_strings":["Ministry of Natural Resources,Key Lab. of Coal Resources Exploration and Comprehensive Utilization,Xi&#x2019;an,China,710021"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ministry of Natural Resources,Key Lab. of Coal Resources Exploration and Comprehensive Utilization,Xi&#x2019;an,China,710021","institution_ids":["https://openalex.org/I211433327","https://openalex.org/I4405273276"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114221444","display_name":"Chenhao Qin","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenhao Qin","raw_affiliation_strings":["Xidian University,School of Artificial Intelligence,Xi&#x2019;an,China,710071"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,School of Artificial Intelligence,Xi&#x2019;an,China,710071","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111963534","display_name":"Wenbo Ji","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenbo Ji","raw_affiliation_strings":["Xidian University,School of Artificial Intelligence,Xi&#x2019;an,China,710071"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xidian University,School of Artificial Intelligence,Xi&#x2019;an,China,710071","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.32986579,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"8229","last_page":"8233"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9955999851226807,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9955999851226807,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.0019000000320374966,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.000699999975040555,"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/water-body-detection","display_name":"water body detection","score":0.9929999709129333},{"id":"https://openalex.org/keywords/very-high-resolution-remote-sensing-images","display_name":"very high resolution remote sensing images","score":0.7599999904632568},{"id":"https://openalex.org/keywords/segment-anything-model-2","display_name":"Segment Anything Model 2","score":0.9710000157356262},{"id":"https://openalex.org/keywords/point-prompts","display_name":"point prompts","score":0.8820000290870667},{"id":"https://openalex.org/keywords/fine-tuning","display_name":"fine-tuning","score":0.8230000138282776},{"id":"https://openalex.org/keywords/semantic-segmentation","display_name":"semantic segmentation","score":0.5870000123977661},{"id":"https://openalex.org/keywords/water-body-segmentation","display_name":"water body segmentation","score":0.375}],"concepts":[{"id":"https://openalex.org/C2986309107","wikidata":"https://www.wikidata.org/wiki/Q15324","display_name":"Water body","level":2,"score":0.7627000212669373},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6851000189781189},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6682000160217285},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6482999920845032},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.6470999717712402},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5570999979972839},{"id":"https://openalex.org/C22029948","wikidata":"https://www.wikidata.org/wiki/Q45089","display_name":"Dice","level":2,"score":0.5482000112533569},{"id":"https://openalex.org/C4725764","wikidata":"https://www.wikidata.org/wiki/Q844704","display_name":"Extraction (chemistry)","level":2,"score":0.5450000166893005},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.5153999924659729},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.47929999232292175},{"id":"https://openalex.org/C2778395939","wikidata":"https://www.wikidata.org/wiki/Q2160967","display_name":"Water extraction","level":3,"score":0.4327999949455261},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.40700000524520874},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3499000072479248},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.34529998898506165},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.3133000135421753},{"id":"https://openalex.org/C183365957","wikidata":"https://www.wikidata.org/wiki/Q17140402","display_name":"Remote sensing application","level":3,"score":0.3124000132083893},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.30869999527931213},{"id":"https://openalex.org/C3020199158","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"High resolution","level":2,"score":0.30469998717308044},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.2533999979496002},{"id":"https://openalex.org/C3019883945","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Low resolution","level":3,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss55030.2025.11243958","doi":"https://doi.org/10.1109/igarss55030.2025.11243958","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss55030.2025.11243958","pdf_url":null,"source":{"id":"https://openalex.org/S4363604196","display_name":"IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2025 - 2025 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Clean water and sanitation","id":"https://metadata.un.org/sdg/6","score":0.7778000235557556}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2560023338","https://openalex.org/W3213992983","https://openalex.org/W4206571460","https://openalex.org/W4387058714","https://openalex.org/W4390874575","https://openalex.org/W4393146401","https://openalex.org/W4394683557","https://openalex.org/W4400764581","https://openalex.org/W4402260833","https://openalex.org/W4402261417","https://openalex.org/W4402704553","https://openalex.org/W4404501750","https://openalex.org/W4410435991"],"related_works":["https://openalex.org/W6921841899","https://openalex.org/W7036817749","https://openalex.org/W7108756508","https://openalex.org/W4414151906","https://openalex.org/W2981719849","https://openalex.org/W3159658500","https://openalex.org/W4400586079","https://openalex.org/W3163489199","https://openalex.org/W7160841703","https://openalex.org/W4414165886"],"abstract_inverted_index":{"Deep":[0],"learning":[1,20],"algorithms":[2,118],"are":[3],"becoming":[4],"increasingly":[5],"important":[6],"for":[7,72,109,128],"water":[8,43,74,110,131],"body":[9,44,75,111],"extraction":[10,112,132],"from":[11],"very-high-resolution":[12],"(VHR)":[13],"remote":[14],"sensing":[15],"images.":[16],"However,":[17],"existing":[18,35],"deep":[19],"models":[21],"require":[22],"a":[23,50,55],"large":[24],"amount":[25],"of":[26,90,105],"high-quality":[27],"labeled":[28],"data.":[29],"Therefore,":[30],"how":[31],"to":[32,41,87],"effectively":[33],"use":[34],"low-resolution":[36],"(LR)":[37],"images":[38,48,80,92],"and":[39,68,81,102,107],"labels":[40,82],"guide":[42,88],"segmentation":[45,89],"in":[46],"VHR":[47,91,114],"is":[49],"challenge.":[51],"This":[52,123],"study":[53],"proposes":[54],"new":[56],"method":[57,99],"that":[58],"fine-tunes":[59],"the":[60,120],"Segment":[61],"Anything":[62],"Model":[63],"2":[64],"(SAM2)":[65],"using":[66],"positive":[67],"negative":[69],"point":[70],"prompts":[71],"cross-scale":[73,130],"segmentation.":[76],"It":[77],"uses":[78],"LR":[79],"at":[83,93],"10":[84],"m":[85,95],"resolution":[86],"0.3":[94],"resolution.":[96],"The":[97],"proposed":[98],"achieves":[100],"Dice":[101],"MIOU":[103],"values":[104],"0.932":[106],"0.906":[108],"on":[113],"images,":[115],"outperforming":[116],"comparison":[117],"with":[119],"best":[121],"performance.":[122],"research":[124],"provides":[125],"technical":[126],"support":[127],"further":[129],"studies.":[133]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2025-11-25T00:00:00"}
