{"id":"https://openalex.org/W4285100848","doi":"https://doi.org/10.1109/comm54429.2022.9817330","title":"SAR Image Classification Using Mixed Spatial-Spectral Information and Pre-trained Convolutional Neural Networks","display_name":"SAR Image Classification Using Mixed Spatial-Spectral Information and Pre-trained Convolutional Neural Networks","publication_year":2022,"publication_date":"2022-06-16","ids":{"openalex":"https://openalex.org/W4285100848","doi":"https://doi.org/10.1109/comm54429.2022.9817330"},"language":"en","primary_location":{"id":"doi:10.1109/comm54429.2022.9817330","is_oa":false,"landing_page_url":"https://doi.org/10.1109/comm54429.2022.9817330","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 14th International Conference on Communications (COMM)","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/A5009867861","display_name":"Melisa \u00dcnsalan","orcid":null},"institutions":[{"id":"https://openalex.org/I61641377","display_name":"Universitatea Na\u021bional\u0103 de \u0218tiin\u021b\u0103 \u0219i Tehnologie Politehnica Bucure\u0219ti","ror":"https://ror.org/0558j5q12","country_code":"RO","type":"education","lineage":["https://openalex.org/I61641377"]}],"countries":["RO"],"is_corresponding":true,"raw_author_name":"Melisa Unsalan","raw_affiliation_strings":["University Politehnica of Bucharest,Department EAII,Bucharest,Romania","Department EAII, University Politehnica of Bucharest, Bucharest, Romania"],"affiliations":[{"raw_affiliation_string":"University Politehnica of Bucharest,Department EAII,Bucharest,Romania","institution_ids":["https://openalex.org/I61641377"]},{"raw_affiliation_string":"Department EAII, University Politehnica of Bucharest, Bucharest, Romania","institution_ids":["https://openalex.org/I61641377"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091483225","display_name":"Anamaria R\u0103doi","orcid":"https://orcid.org/0000-0002-7577-1067"},"institutions":[{"id":"https://openalex.org/I61641377","display_name":"Universitatea Na\u021bional\u0103 de \u0218tiin\u021b\u0103 \u0219i Tehnologie Politehnica Bucure\u0219ti","ror":"https://ror.org/0558j5q12","country_code":"RO","type":"education","lineage":["https://openalex.org/I61641377"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Anamaria Radoi","raw_affiliation_strings":["University Politehnica of Bucharest,Department EAII,Bucharest,Romania","Department EAII, University Politehnica of Bucharest, Bucharest, Romania"],"affiliations":[{"raw_affiliation_string":"University Politehnica of Bucharest,Department EAII,Bucharest,Romania","institution_ids":["https://openalex.org/I61641377"]},{"raw_affiliation_string":"Department EAII, University Politehnica of Bucharest, Bucharest, Romania","institution_ids":["https://openalex.org/I61641377"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5032370631","display_name":"Mihai Datcu","orcid":"https://orcid.org/0000-0002-3477-9687"},"institutions":[{"id":"https://openalex.org/I61641377","display_name":"Universitatea Na\u021bional\u0103 de \u0218tiin\u021b\u0103 \u0219i Tehnologie Politehnica Bucure\u0219ti","ror":"https://ror.org/0558j5q12","country_code":"RO","type":"education","lineage":["https://openalex.org/I61641377"]}],"countries":["RO"],"is_corresponding":false,"raw_author_name":"Mihai Datcu","raw_affiliation_strings":["University Politehnica of Bucharest,Department EAII,Bucharest,Romania","Department EAII, University Politehnica of Bucharest, Bucharest, Romania"],"affiliations":[{"raw_affiliation_string":"University Politehnica of Bucharest,Department EAII,Bucharest,Romania","institution_ids":["https://openalex.org/I61641377"]},{"raw_affiliation_string":"Department EAII, University Politehnica of Bucharest, Bucharest, Romania","institution_ids":["https://openalex.org/I61641377"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5009867861"],"corresponding_institution_ids":["https://openalex.org/I61641377"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.02528125,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"abs 1811 3378","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9998000264167786,"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"}},"topics":[{"id":"https://openalex.org/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9998000264167786,"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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.9990000128746033,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/computer-science","display_name":"Computer science","score":0.8021587133407593},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7031761407852173},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6745163202285767},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.6527759432792664},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6134964227676392},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.5073303580284119},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.48911768198013306},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.4768610894680023},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.47162795066833496},{"id":"https://openalex.org/keywords/radar-imaging","display_name":"Radar imaging","score":0.467325359582901},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.4504299759864807},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4236517548561096},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4199206531047821},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2827809453010559},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.09476080536842346}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8021587133407593},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7031761407852173},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6745163202285767},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.6527759432792664},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6134964227676392},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.5073303580284119},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.48911768198013306},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.4768610894680023},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.47162795066833496},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.467325359582901},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.4504299759864807},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4236517548561096},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4199206531047821},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2827809453010559},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.09476080536842346},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/comm54429.2022.9817330","is_oa":false,"landing_page_url":"https://doi.org/10.1109/comm54429.2022.9817330","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2022 14th International Conference on Communications (COMM)","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/W1522301498","https://openalex.org/W2108598243","https://openalex.org/W2112101337","https://openalex.org/W2117539524","https://openalex.org/W2133989913","https://openalex.org/W2164296623","https://openalex.org/W2187089797","https://openalex.org/W2194775991","https://openalex.org/W2899663614","https://openalex.org/W2899675781","https://openalex.org/W2954579674","https://openalex.org/W2998270553","https://openalex.org/W3001606623","https://openalex.org/W3048923904","https://openalex.org/W3128476715","https://openalex.org/W3211012720","https://openalex.org/W6631190155","https://openalex.org/W6687483927","https://openalex.org/W6755977528","https://openalex.org/W6756026151","https://openalex.org/W6772025812","https://openalex.org/W6803523347"],"related_works":["https://openalex.org/W2545123933","https://openalex.org/W2952813363","https://openalex.org/W2585813813","https://openalex.org/W2041414401","https://openalex.org/W2042726296","https://openalex.org/W2096748030","https://openalex.org/W3016428515","https://openalex.org/W2160730947","https://openalex.org/W2747205507","https://openalex.org/W2917196883"],"abstract_inverted_index":{"The":[0,172],"recent":[1],"technological":[2],"advancements":[3],"in":[4,35,50,64,90,96],"remote":[5,52,101],"sensing":[6,53,102],"lead":[7],"to":[8,167],"an":[9,93],"increased":[10],"importance":[11],"regarding":[12,130],"the":[13,24,33,51,97,100,131,136,141,145,161,185],"analysis":[14],"of":[15,26,86,99,120,135,144,160],"satellite":[16],"data":[17,27],"targeting":[18],"security":[19],"and":[20,32,114,133],"surveillance":[21],"tasks.":[22],"Although":[23],"availability":[25],"products":[28],"is":[29],"constantly":[30],"augmented":[31],"advances":[34],"Deep":[36],"Learning":[37],"technologies":[38],"are":[39,68,147,165,174,179],"constant,":[40],"Synthetic":[41],"Aperture":[42],"Radar":[43],"(SAR)":[44],"image":[45,109],"classification":[46,110],"remains":[47],"a":[48,83],"challenge":[49],"domain":[54],"because":[55],"standard":[56],"convolutional":[57],"neural":[58,122],"network-based":[59],"architectures":[60,81],"may":[61],"encounter":[62],"difficulties":[63],"recognizing":[65],"objects":[66,137,146],"that":[67,156],"characterized":[69],"by":[70,118],"similar":[71],"texture,":[72],"but":[73],"different":[74,158],"backscattering":[75],"patterns.":[76],"Moreover,":[77],"training":[78],"deep":[79],"learning":[80],"requires":[82],"large":[84],"volume":[85],"annotated":[87],"data,":[88],"which,":[89],"general,":[91],"represents":[92],"obstacle,":[94],"especially":[95],"case":[98],"domain.":[103],"This":[104],"article":[105],"addresses":[106],"complex-valued":[107],"SAR":[108,162],"through":[111],"both":[112],"spatial":[113,125],"Fourier-domain":[115],"features,":[116],"extracted":[117],"means":[119],"pretrained":[121],"networks.":[123],"While":[124],"features":[126],"allow":[127],"extracting":[128],"knowl-edge":[129],"structure":[132],"texture":[134],"from":[138,149],"intensity":[139],"images,":[140],"physical":[142],"properties":[143],"learned":[148],"radar":[150],"spectrograms.":[151],"In":[152],"addition,":[153],"we":[154,164],"show":[155],"considering":[157],"polarizations":[159],"sensor,":[163],"able":[166],"obtain":[168],"better":[169],"visual":[170],"classifications.":[171],"experiments":[173],"conducted":[175],"over":[176],"Sentinel-1images,":[177],"which":[178],"freely":[180],"available":[181],"for":[182],"download":[183],"under":[184],"Copernicus":[186],"initiative.":[187]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}