{"id":"https://openalex.org/W3205975767","doi":"https://doi.org/10.1109/igarss47720.2021.9554303","title":"Polsar Image Classification with Complex-Valued Residual Attention Enhanced U-NET","display_name":"Polsar Image Classification with Complex-Valued Residual Attention Enhanced U-NET","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3205975767","doi":"https://doi.org/10.1109/igarss47720.2021.9554303","mag":"3205975767"},"language":"en","primary_location":{"id":"doi:10.1109/igarss47720.2021.9554303","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9554303","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","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/A5103017516","display_name":"Shijie Ren","orcid":"https://orcid.org/0000-0002-1764-7068"},"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":true,"raw_author_name":"Shijie Ren","raw_affiliation_strings":["Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5080509511","display_name":"Feng Zhou","orcid":"https://orcid.org/0000-0002-1514-7393"},"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":"Feng Zhou","raw_affiliation_strings":["Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, Xidian University, Xi'an, China"],"affiliations":[{"raw_affiliation_string":"Key Laboratory of Electronic Information Countermeasure and Simulation Technology of Ministry of Education, Xidian University, Xi'an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5103017516"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":2.72,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.90337893,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"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":0.9998999834060669,"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":0.9998999834060669,"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":0.9991000294685364,"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/T12549","display_name":"Image and Object Detection Techniques","score":0.988099992275238,"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/residual","display_name":"Residual","score":0.7702397108078003},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.7630161046981812},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7377335429191589},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6985897421836853},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6187853813171387},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5446597337722778},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.5010969638824463},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.498431921005249},{"id":"https://openalex.org/keywords/radar-imaging","display_name":"Radar imaging","score":0.49160298705101013},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4586943984031677},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.42019179463386536},{"id":"https://openalex.org/keywords/net","display_name":"Net (polyhedron)","score":0.4150190055370331},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.36037254333496094},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.3511207103729248},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.221589595079422},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10477712750434875}],"concepts":[{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.7702397108078003},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.7630161046981812},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7377335429191589},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6985897421836853},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6187853813171387},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5446597337722778},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5010969638824463},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.498431921005249},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.49160298705101013},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4586943984031677},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.42019179463386536},{"id":"https://openalex.org/C14166107","wikidata":"https://www.wikidata.org/wiki/Q253829","display_name":"Net (polyhedron)","level":2,"score":0.4150190055370331},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.36037254333496094},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.3511207103729248},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.221589595079422},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10477712750434875},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss47720.2021.9554303","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9554303","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7400000095367432}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":12,"referenced_works":["https://openalex.org/W2168894214","https://openalex.org/W2194775991","https://openalex.org/W2345055757","https://openalex.org/W2610921225","https://openalex.org/W2754361766","https://openalex.org/W2884436604","https://openalex.org/W2900677911","https://openalex.org/W2910641067","https://openalex.org/W2963495494","https://openalex.org/W2989067579","https://openalex.org/W3015788359","https://openalex.org/W6684665197"],"related_works":["https://openalex.org/W2545123933","https://openalex.org/W2160730947","https://openalex.org/W2585813813","https://openalex.org/W3110962985","https://openalex.org/W2042726296","https://openalex.org/W2081203575","https://openalex.org/W1908997176","https://openalex.org/W3016428515","https://openalex.org/W2887270943","https://openalex.org/W2098441753"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"an":[3,54],"end-to-end":[4],"classification":[5],"method":[6,76],"for":[7,57],"polarimetric":[8],"synthetic":[9],"aperture":[10],"radar":[11],"(PoISAR)":[12],"images":[13],"through":[14],"complex-valued":[15,24],"residual":[16,31],"attention":[17,32],"enhanced":[18],"U-Net":[19],"is":[20],"proposed,":[21],"which":[22],"incorporates":[23],"operation":[25],"to":[26,34,52],"utilize":[27],"phase":[28],"information":[29],"and":[30,64,92],"modules":[33],"enhance":[35,49],"discriminate":[36],"features":[37],"in":[38],"multiple":[39],"resolutions.":[40],"Besides,":[41],"the":[42,50,66],"deep":[43,88],"supervision":[44],"strategy":[45],"can":[46,77],"not":[47],"only":[48],"ability":[51],"learn":[53],"effective":[55],"representation":[56],"each":[58],"scale,":[59],"but":[60],"also":[61],"speed":[62],"up":[63],"stabilize":[65],"training":[67],"process.":[68],"The":[69],"experiments":[70],"clearly":[71],"demonstrate":[72],"that":[73],"our":[74],"proposed":[75,84],"achieve":[78],"state-of-the-art":[79],"performance":[80],"compared":[81],"with":[82],"recently":[83],"approaches":[85],"based":[86],"on":[87],"belief":[89],"networks,":[90],"autoencoders":[91],"convolutional":[93],"neural":[94],"networks.":[95]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
