{"id":"https://openalex.org/W4402263812","doi":"https://doi.org/10.1109/igarss53475.2024.10642526","title":"A Semantic Segmentation-Guided Approach for Ground-to-Aerial Image Matching","display_name":"A Semantic Segmentation-Guided Approach for Ground-to-Aerial Image Matching","publication_year":2024,"publication_date":"2024-07-07","ids":{"openalex":"https://openalex.org/W4402263812","doi":"https://doi.org/10.1109/igarss53475.2024.10642526"},"language":"en","primary_location":{"id":"doi:10.1109/igarss53475.2024.10642526","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss53475.2024.10642526","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://hdl.handle.net/11573/1727944","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5095777597","display_name":"Francesco Pro","orcid":null},"institutions":[{"id":"https://openalex.org/I861853513","display_name":"Sapienza University of Rome","ror":"https://ror.org/02be6w209","country_code":"IT","type":"education","lineage":["https://openalex.org/I861853513"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Francesco Pro","raw_affiliation_strings":["Sapienza University of Rome,Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sapienza University of Rome,Italy","institution_ids":["https://openalex.org/I861853513"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052528008","display_name":"Nikolaos Dionelis","orcid":"https://orcid.org/0000-0001-9662-8537"},"institutions":[{"id":"https://openalex.org/I4210165017","display_name":"European Space Research Institute","ror":"https://ror.org/05vt9rv16","country_code":"IT","type":"facility","lineage":["https://openalex.org/I2801994115","https://openalex.org/I4210165017"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Nikolaos Dionelis","raw_affiliation_strings":["European Space Agency (ESA),ESRIN, Φ-lab,Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"European Space Agency (ESA),ESRIN, Φ-lab,Italy","institution_ids":["https://openalex.org/I4210165017"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066029199","display_name":"Luca Maiano","orcid":"https://orcid.org/0000-0001-7969-7821"},"institutions":[{"id":"https://openalex.org/I861853513","display_name":"Sapienza University of Rome","ror":"https://ror.org/02be6w209","country_code":"IT","type":"education","lineage":["https://openalex.org/I861853513"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Luca Maiano","raw_affiliation_strings":["Sapienza University of Rome,Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sapienza University of Rome,Italy","institution_ids":["https://openalex.org/I861853513"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5067965505","display_name":"Bertrand Le Saux","orcid":"https://orcid.org/0000-0001-7162-6746"},"institutions":[{"id":"https://openalex.org/I4210165017","display_name":"European Space Research Institute","ror":"https://ror.org/05vt9rv16","country_code":"IT","type":"facility","lineage":["https://openalex.org/I2801994115","https://openalex.org/I4210165017"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Bertrand Le Saux","raw_affiliation_strings":["European Space Agency (ESA),ESRIN, Φ-lab,Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"European Space Agency (ESA),ESRIN, Φ-lab,Italy","institution_ids":["https://openalex.org/I4210165017"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030121038","display_name":"Irene Amerini","orcid":"https://orcid.org/0000-0002-6461-1391"},"institutions":[{"id":"https://openalex.org/I861853513","display_name":"Sapienza University of Rome","ror":"https://ror.org/02be6w209","country_code":"IT","type":"education","lineage":["https://openalex.org/I861853513"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Irene Amerini","raw_affiliation_strings":["Sapienza University of Rome,Italy"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sapienza University of Rome,Italy","institution_ids":["https://openalex.org/I861853513"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2630","last_page":"2635"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9997000098228455,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9997000098228455,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.9991000294685364,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9980999827384949,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.7053340673446655},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6807538270950317},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.6458839178085327},{"id":"https://openalex.org/keywords/aerial-image","display_name":"Aerial image","score":0.6133197546005249},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.6051864624023438},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5807410478591919},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5723093748092651},{"id":"https://openalex.org/keywords/image-matching","display_name":"Image matching","score":0.5261475443840027},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.49968576431274414},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.44473952054977417},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.423137903213501},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1218612790107727},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.05042201280593872}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7053340673446655},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6807538270950317},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.6458839178085327},{"id":"https://openalex.org/C2776429412","wikidata":"https://www.wikidata.org/wiki/Q4688011","display_name":"Aerial image","level":3,"score":0.6133197546005249},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.6051864624023438},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5807410478591919},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5723093748092651},{"id":"https://openalex.org/C2986492983","wikidata":"https://www.wikidata.org/wiki/Q861092","display_name":"Image matching","level":3,"score":0.5261475443840027},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.49968576431274414},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.44473952054977417},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.423137903213501},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1218612790107727},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.05042201280593872}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/igarss53475.2024.10642526","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss53475.2024.10642526","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"},{"id":"pmh:oai:iris.uniroma1.it:11573/1727944","is_oa":true,"landing_page_url":"https://hdl.handle.net/11573/1727944","pdf_url":null,"source":{"id":"https://openalex.org/S4377196107","display_name":"IRIS Research product catalog (Sapienza University of Rome)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"}],"best_oa_location":{"id":"pmh:oai:iris.uniroma1.it:11573/1727944","is_oa":true,"landing_page_url":"https://hdl.handle.net/11573/1727944","pdf_url":null,"source":{"id":"https://openalex.org/S4377196107","display_name":"IRIS Research product catalog (Sapienza University of Rome)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/conferenceObject"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W104903125","https://openalex.org/W1969891195","https://openalex.org/W1987488988","https://openalex.org/W2103163130","https://openalex.org/W2199890863","https://openalex.org/W2572697301","https://openalex.org/W2613201584","https://openalex.org/W2799087793","https://openalex.org/W2916093327","https://openalex.org/W2981670313","https://openalex.org/W2997129483","https://openalex.org/W3035158519","https://openalex.org/W3058492236","https://openalex.org/W3129983770","https://openalex.org/W3174800995","https://openalex.org/W3211490618","https://openalex.org/W3214730767","https://openalex.org/W4213144930","https://openalex.org/W4293518658","https://openalex.org/W4319300728","https://openalex.org/W4383219126","https://openalex.org/W4402260041","https://openalex.org/W6759842922","https://openalex.org/W6797399245"],"related_works":["https://openalex.org/W2185902295","https://openalex.org/W2103507220","https://openalex.org/W3144569342","https://openalex.org/W2945274617","https://openalex.org/W4313052709","https://openalex.org/W4205800335","https://openalex.org/W2022929107","https://openalex.org/W2055202857","https://openalex.org/W80586315","https://openalex.org/W2758994127"],"abstract_inverted_index":{"Nowadays":[0],"the":[1,26,35,47,59,95,110,119,123,136,139,145],"accurate":[2],"geo-localization":[3],"of":[4,28,89,97,122,133],"ground-view":[5,32,51],"images":[6,84,99,134],"has":[7],"an":[8],"important":[9],"role":[10],"across":[11],"domains":[12],"as":[13,15],"diverse":[14],"journalism,":[16],"forensics":[17],"analysis,":[18],"transports,":[19],"and":[20,53,81,116],"Earth":[21],"Observation.":[22],"This":[23,42,125],"work":[24,126],"addresses":[25],"problem":[27],"matching":[29],"a":[30,50,54,65,87],"query":[31],"image":[33,38,52],"with":[34,86,102],"corresponding":[36,60],"satellite":[37,55,98],"without":[39],"GPS":[40],"data.":[41],"is":[43],"done":[44],"by":[45],"comparing":[46],"features":[48,115],"from":[49],"one,":[56],"innovatively":[57],"leveraging":[58],"latter\u2019s":[61],"segmentation":[62,104],"mask":[63],"through":[64,130],"three-stream":[66],"Siamese-like":[67],"network.":[68],"The":[69,91],"proposed":[70],"method,":[71],"Semantic":[72],"Align":[73],"Net":[74],"(SAN),":[75],"focuses":[76],"on":[77,118,138],"limited":[78],"Field-of-View":[79],"(FoV)":[80],"ground":[82],"panorama":[83],"(images":[85],"FoV":[88],"360\u00b0).":[90],"novelty":[92],"lies":[93],"in":[94,100],"fusion":[96],"combination":[101],"their":[103],"masks,":[105],"aimed":[106],"at":[107],"ensuring":[108],"that":[109],"model":[111],"can":[112],"extract":[113],"useful":[114],"focus":[117],"significant":[120],"parts":[121],"images.":[124],"shows":[127],"how":[128],"SAN":[129],"semantic":[131],"analysis":[132],"improves":[135],"performance":[137],"unlabelled":[140],"CVUSA":[141],"dataset":[142],"for":[143],"all":[144],"tested":[146],"FoVs.":[147]},"counts_by_year":[{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2024-09-06T00:00:00"}