{"id":"https://openalex.org/W2773517595","doi":"https://doi.org/10.1109/igarss.2017.8127693","title":"Sea ice and open water classification of sar imagery using cnn-based transfer learning","display_name":"Sea ice and open water classification of sar imagery using cnn-based transfer learning","publication_year":2017,"publication_date":"2017-07-01","ids":{"openalex":"https://openalex.org/W2773517595","doi":"https://doi.org/10.1109/igarss.2017.8127693","mag":"2773517595"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2017.8127693","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2017.8127693","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","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/A5100613351","display_name":"Yan Xu","orcid":"https://orcid.org/0000-0001-5886-9938"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"Yan Xu","raw_affiliation_strings":["Department of Systems Design Engineering, University of Waterloo, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Systems Design Engineering, University of Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083200788","display_name":"K. Andrea Scott","orcid":"https://orcid.org/0000-0003-3922-8777"},"institutions":[{"id":"https://openalex.org/I151746483","display_name":"University of Waterloo","ror":"https://ror.org/01aff2v68","country_code":"CA","type":"education","lineage":["https://openalex.org/I151746483"]}],"countries":["CA"],"is_corresponding":false,"raw_author_name":"K. Andrea Scott","raw_affiliation_strings":["Department of Systems Design Engineering, University of Waterloo, Canada"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Systems Design Engineering, University of Waterloo, Canada","institution_ids":["https://openalex.org/I151746483"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I151746483"],"apc_list":null,"apc_paid":null,"fwci":19.9742,"has_fulltext":false,"cited_by_count":35,"citation_normalized_percentile":{"value":0.9956392,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3262","last_page":"3265"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11459","display_name":"Arctic and Antarctic ice dynamics","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11459","display_name":"Arctic and Antarctic ice dynamics","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10995","display_name":"Methane Hydrates and Related Phenomena","score":0.988099992275238,"subfield":{"id":"https://openalex.org/subfields/2304","display_name":"Environmental Chemistry"},"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/T11698","display_name":"Underwater Acoustics Research","score":0.9800000190734863,"subfield":{"id":"https://openalex.org/subfields/1910","display_name":"Oceanography"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/softmax-function","display_name":"Softmax function","score":0.9234713912010193},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7812644839286804},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.6967469453811646},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6940430998802185},{"id":"https://openalex.org/keywords/transfer-of-learning","display_name":"Transfer of learning","score":0.6629495024681091},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6462958455085754},{"id":"https://openalex.org/keywords/sea-ice","display_name":"Sea ice","score":0.5315520763397217},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5267552733421326},{"id":"https://openalex.org/keywords/radar-imaging","display_name":"Radar imaging","score":0.4922906458377838},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4886968433856964},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48724380135536194},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4851894974708557},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.46110326051712036},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.32819920778274536},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.27868083119392395},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.23572638630867004},{"id":"https://openalex.org/keywords/oceanography","display_name":"Oceanography","score":0.07930958271026611}],"concepts":[{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.9234713912010193},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7812644839286804},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.6967469453811646},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6940430998802185},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.6629495024681091},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6462958455085754},{"id":"https://openalex.org/C136894858","wikidata":"https://www.wikidata.org/wiki/Q213926","display_name":"Sea ice","level":2,"score":0.5315520763397217},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5267552733421326},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.4922906458377838},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4886968433856964},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48724380135536194},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4851894974708557},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.46110326051712036},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.32819920778274536},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.27868083119392395},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.23572638630867004},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.07930958271026611},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2017.8127693","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2017.8127693","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7900000214576721,"id":"https://metadata.un.org/sdg/14","display_name":"Life below water"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1980508351","https://openalex.org/W2163605009","https://openalex.org/W2253590344","https://openalex.org/W2319040573","https://openalex.org/W2512745809","https://openalex.org/W2962835968","https://openalex.org/W6637373629","https://openalex.org/W6684191040","https://openalex.org/W6725756066"],"related_works":["https://openalex.org/W3131922633","https://openalex.org/W4229443568","https://openalex.org/W3176438653","https://openalex.org/W2981628807","https://openalex.org/W3012393889","https://openalex.org/W4379875147","https://openalex.org/W3189091156","https://openalex.org/W4386087993","https://openalex.org/W3014041368","https://openalex.org/W4285815841"],"abstract_inverted_index":{"Mapping":[0],"sea":[1,20],"ice":[2,21,29,84],"and":[3,15,22,50,85,101,118],"open":[4,23],"water":[5,24],"in":[6,26,39,104],"the":[7,72,114,134],"oceans":[8],"is":[9],"significant":[10],"for":[11,71],"many":[12,40,63],"applications.":[13],"Accurate":[14],"robust":[16],"classification":[17,65,73,128],"methods":[18],"of":[19,46,74,113,130],"are":[25,35],"demand":[27],"by":[28],"services.":[30],"Convolutional":[31,55],"neural":[32],"networks":[33,56],"(CNNs)":[34],"becoming":[36],"increasingly":[37],"popular":[38],"research":[41],"communities":[42],"due":[43],"to":[44],"availability":[45],"large":[47],"image":[48,64,75,92],"datasets":[49],"high-performance":[51],"computing":[52],"systems.":[53],"As":[54],"(ConvNets)":[57],"have":[58],"achieved":[59],"great":[60],"success":[61],"on":[62,133],"tasks,":[66],"we":[67,90],"pursue":[68],"this":[69,88],"method":[70,108,124],"patches":[76,93,115],"from":[77,116],"synthetic":[78],"aperture":[79],"radar":[80],"(SAR)":[81],"imagery":[82],"into":[83],"water.":[86],"In":[87],"study":[89],"use":[91],"with":[94,109],"three":[95],"dimensions":[96],"(HH":[97],"polarization,":[98,100],"HV":[99],"incidence":[102],"angle)":[103],"a":[105,120],"transfer":[106],"learning":[107],"CNNs:":[110],"extracting":[111],"features":[112],"AlexNet":[117],"applying":[119],"softmax":[121],"classifier.":[122],"Our":[123],"achieves":[125],"an":[126],"overall":[127],"accuracy":[129],"92.36%":[131],"based":[132],"held-out":[135],"test":[136],"data.":[137]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":4},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":4},{"year":2018,"cited_by_count":4}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}