{"id":"https://openalex.org/W4392902973","doi":"https://doi.org/10.1109/icassp48485.2024.10448409","title":"SAR2NDVI: Pre-Training for SAR-to-NDVI Image Translation","display_name":"SAR2NDVI: Pre-Training for SAR-to-NDVI Image Translation","publication_year":2024,"publication_date":"2024-03-18","ids":{"openalex":"https://openalex.org/W4392902973","doi":"https://doi.org/10.1109/icassp48485.2024.10448409"},"language":"en","primary_location":{"id":"doi:10.1109/icassp48485.2024.10448409","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10448409","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5047548171","display_name":"Daiki Kimura","orcid":"https://orcid.org/0000-0001-5180-1949"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Daiki Kimura","raw_affiliation_strings":["IBM Research"],"affiliations":[{"raw_affiliation_string":"IBM Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007492003","display_name":"Tatsuya Ishikawa","orcid":"https://orcid.org/0000-0003-3553-6510"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tatsuya Ishikawa","raw_affiliation_strings":["IBM Research"],"affiliations":[{"raw_affiliation_string":"IBM Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109692703","display_name":"Masanori Mitsugi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210145865","display_name":"IBM Research - Tokyo","ror":"https://ror.org/04915qk43","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210145865"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Masanori Mitsugi","raw_affiliation_strings":["IBM Japan Ltd"],"affiliations":[{"raw_affiliation_string":"IBM Japan Ltd","institution_ids":["https://openalex.org/I4210145865"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015220842","display_name":"Yasunori Kitakoshi","orcid":null},"institutions":[{"id":"https://openalex.org/I4210145865","display_name":"IBM Research - Tokyo","ror":"https://ror.org/04915qk43","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210145865"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Yasunori Kitakoshi","raw_affiliation_strings":["IBM Japan Digital Services Company"],"affiliations":[{"raw_affiliation_string":"IBM Japan Digital Services Company","institution_ids":["https://openalex.org/I4210145865"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016902367","display_name":"Takahiro Tanaka","orcid":"https://orcid.org/0000-0003-4174-2214"},"institutions":[{"id":"https://openalex.org/I4210145865","display_name":"IBM Research - Tokyo","ror":"https://ror.org/04915qk43","country_code":"JP","type":"facility","lineage":["https://openalex.org/I1341412227","https://openalex.org/I4210114115","https://openalex.org/I4210145865"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Takahiro Tanaka","raw_affiliation_strings":["IBM Japan Ltd"],"affiliations":[{"raw_affiliation_string":"IBM Japan Ltd","institution_ids":["https://openalex.org/I4210145865"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039989221","display_name":"Naomi Simumba","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Naomi Simumba","raw_affiliation_strings":["IBM Research"],"affiliations":[{"raw_affiliation_string":"IBM Research","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101526122","display_name":"Kentaro Tanaka","orcid":"https://orcid.org/0000-0003-4602-4465"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kentaro Tanaka","raw_affiliation_strings":["Space Shift Inc"],"affiliations":[{"raw_affiliation_string":"Space Shift Inc","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109942239","display_name":"H. Wakabayashi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hiroaki Wakabayashi","raw_affiliation_strings":["Space Shift Inc"],"affiliations":[{"raw_affiliation_string":"Space Shift Inc","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109692704","display_name":"Masato Sampei","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Masato Sampei","raw_affiliation_strings":["Space Shift Inc"],"affiliations":[{"raw_affiliation_string":"Space Shift Inc","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049672783","display_name":"Michiaki Tatsubori","orcid":"https://orcid.org/0000-0003-2537-700X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Michiaki Tatsubori","raw_affiliation_strings":["IBM Research"],"affiliations":[{"raw_affiliation_string":"IBM Research","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":10,"corresponding_author_ids":["https://openalex.org/A5047548171"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.4081,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.83094531,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"3865","last_page":"3869"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10535","display_name":"Landslides and related hazards","score":0.9890999794006348,"subfield":{"id":"https://openalex.org/subfields/2308","display_name":"Management, Monitoring, Policy and Law"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10535","display_name":"Landslides and related hazards","score":0.9890999794006348,"subfield":{"id":"https://openalex.org/subfields/2308","display_name":"Management, Monitoring, Policy and Law"},"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/T10930","display_name":"Flood Risk Assessment and Management","score":0.9850000143051147,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"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/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9778000116348267,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/geospatial-analysis","display_name":"Geospatial analysis","score":0.7747917771339417},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7100450992584229},{"id":"https://openalex.org/keywords/normalized-difference-vegetation-index","display_name":"Normalized Difference Vegetation Index","score":0.678412675857544},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.6130490303039551},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.5093889832496643},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.48182883858680725},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4276390075683594},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3624604344367981},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.34261471033096313},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.14129099249839783},{"id":"https://openalex.org/keywords/climate-change","display_name":"Climate change","score":0.09779873490333557}],"concepts":[{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.7747917771339417},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7100450992584229},{"id":"https://openalex.org/C1549246","wikidata":"https://www.wikidata.org/wiki/Q718775","display_name":"Normalized Difference Vegetation Index","level":3,"score":0.678412675857544},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6130490303039551},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.5093889832496643},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.48182883858680725},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4276390075683594},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3624604344367981},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.34261471033096313},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.14129099249839783},{"id":"https://openalex.org/C132651083","wikidata":"https://www.wikidata.org/wiki/Q7942","display_name":"Climate change","level":2,"score":0.09779873490333557},{"id":"https://openalex.org/C18903297","wikidata":"https://www.wikidata.org/wiki/Q7150","display_name":"Ecology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp48485.2024.10448409","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp48485.2024.10448409","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W791978432","https://openalex.org/W1901129140","https://openalex.org/W2108598243","https://openalex.org/W2745131289","https://openalex.org/W2806464357","https://openalex.org/W2810923834","https://openalex.org/W2904678720","https://openalex.org/W2963073614","https://openalex.org/W3005406698","https://openalex.org/W3009146599","https://openalex.org/W3047166575","https://openalex.org/W3085271102","https://openalex.org/W3170002058","https://openalex.org/W4206653012","https://openalex.org/W4220953084","https://openalex.org/W4281622172","https://openalex.org/W4309651987","https://openalex.org/W4365452837","https://openalex.org/W4395055639","https://openalex.org/W6622788192","https://openalex.org/W6752062404","https://openalex.org/W6857434634"],"related_works":["https://openalex.org/W3207046288","https://openalex.org/W3023446922","https://openalex.org/W4324030030","https://openalex.org/W4392566681","https://openalex.org/W1980260791","https://openalex.org/W4385533602","https://openalex.org/W4367313141","https://openalex.org/W3189212133","https://openalex.org/W2004086023","https://openalex.org/W4382239404"],"abstract_inverted_index":{"Geospatial":[0],"machine":[1],"learning":[2],"is":[3],"of":[4,16,85,98,116],"growing":[5],"importance":[6],"in":[7,13,107],"various":[8],"global":[9],"remote-sensing":[10],"applications,":[11],"particularly":[12],"the":[14,83,114,117,134],"realm":[15],"vegetation":[17,88],"monitoring.":[18],"However,":[19],"acquiring":[20],"accurate":[21],"ground":[22],"truth":[23],"data":[24,44,56,76],"for":[25],"geospatial":[26,55],"tasks":[27],"remains":[28],"a":[29,64,79,108],"significant":[30],"challenge,":[31],"often":[32],"entailing":[33],"considerable":[34],"time":[35],"and":[36,45,74,133],"effort.":[37],"Foundation":[38],"models,":[39],"emphasizing":[40],"pre-training":[41,71],"on":[42],"large-scale":[43],"fine-tuning,":[46],"show":[47],"promise":[48],"but":[49],"face":[50],"limitations":[51],"when":[52],"applied":[53],"to":[54,58],"due":[57],"domain":[59],"differences.":[60],"Our":[61],"paper":[62],"introduces":[63],"novel":[65],"image":[66,127],"translation":[67,84,128],"method,":[68],"combining":[69],"geospatial-specific":[70,135],"with":[72,130],"training":[73],"test-time":[75],"augmentation.":[77],"In":[78],"case":[80],"study":[81],"involving":[82],"normalized":[86],"difference":[87],"index":[89],"(NDVI)":[90],"values":[91],"from":[92],"synthetic":[93],"aperture":[94],"radar":[95],"(SAR)":[96],"images":[97],"cabbage":[99],"farms,":[100],"our":[101,126],"approach":[102],"outperformed":[103],"competitors":[104],"by":[105,121],"31%":[106],"public":[109],"competition.":[110],"It":[111],"also":[112],"exceeded":[113],"average":[115],"top":[118],"five":[119],"teams":[120],"44%.":[122],"We":[123],"publish":[124],"both":[125],"method":[129],"baseline":[131],"methods":[132],"dataset":[136],"at":[137],"https://github.com/IBM/SAR2NDVI.":[138]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1}],"updated_date":"2026-04-02T15:55:50.835912","created_date":"2025-10-10T00:00:00"}