{"id":"https://openalex.org/W4405022240","doi":"https://doi.org/10.1109/lgrs.2024.3510834","title":"Feature-Supervision Network for Synthetic Aperture Radar Image Despeckling","display_name":"Feature-Supervision Network for Synthetic Aperture Radar Image Despeckling","publication_year":2024,"publication_date":"2024-12-04","ids":{"openalex":"https://openalex.org/W4405022240","doi":"https://doi.org/10.1109/lgrs.2024.3510834"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2024.3510834","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2024.3510834","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-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/A5028766722","display_name":"Bowen Yang","orcid":"https://orcid.org/0000-0002-6305-5565"},"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":"Bowen Yang","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi’an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063069006","display_name":"Guanghui Zhao","orcid":"https://orcid.org/0000-0002-2348-0532"},"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":"Guanghui Zhao","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi’an, China"],"raw_orcid":"https://orcid.org/0000-0002-2348-0532","affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004601638","display_name":"Shuxuan Chen","orcid":null},"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":"Shuxuan Chen","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi’an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5052477187","display_name":"Xianda Zhou","orcid":"https://orcid.org/0000-0002-2736-5291"},"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":"Xianda Zhou","raw_affiliation_strings":["School of Artificial Intelligence, Xidian University, Xi’an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, Xidian University, Xi’an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20448456,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"22","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9890000224113464,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9890000224113464,"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9800000190734863,"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"}},{"id":"https://openalex.org/T10271","display_name":"Seismic Imaging and Inversion Techniques","score":0.9690999984741211,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"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/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.8057534098625183},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6679204106330872},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6623343825340271},{"id":"https://openalex.org/keywords/radar-imaging","display_name":"Radar imaging","score":0.6313636898994446},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5991301536560059},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5032805800437927},{"id":"https://openalex.org/keywords/side-looking-airborne-radar","display_name":"Side looking airborne radar","score":0.49106788635253906},{"id":"https://openalex.org/keywords/inverse-synthetic-aperture-radar","display_name":"Inverse synthetic aperture radar","score":0.489317387342453},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4758338928222656},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.45137491822242737},{"id":"https://openalex.org/keywords/bistatic-radar","display_name":"Bistatic radar","score":0.3822605609893799},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.37378719449043274},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.349173367023468},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.2290562391281128},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.19439080357551575}],"concepts":[{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.8057534098625183},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6679204106330872},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6623343825340271},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.6313636898994446},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5991301536560059},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5032805800437927},{"id":"https://openalex.org/C62282787","wikidata":"https://www.wikidata.org/wiki/Q831483","display_name":"Side looking airborne radar","level":5,"score":0.49106788635253906},{"id":"https://openalex.org/C109094680","wikidata":"https://www.wikidata.org/wiki/Q6060432","display_name":"Inverse synthetic aperture radar","level":4,"score":0.489317387342453},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4758338928222656},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.45137491822242737},{"id":"https://openalex.org/C100102862","wikidata":"https://www.wikidata.org/wiki/Q2625855","display_name":"Bistatic radar","level":4,"score":0.3822605609893799},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.37378719449043274},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.349173367023468},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.2290562391281128},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.19439080357551575},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2024.3510834","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2024.3510834","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2004376198","https://openalex.org/W2057771708","https://openalex.org/W2090563002","https://openalex.org/W2117294245","https://openalex.org/W2131120753","https://openalex.org/W2144851790","https://openalex.org/W2508457857","https://openalex.org/W2573726823","https://openalex.org/W2604403460","https://openalex.org/W2621042270","https://openalex.org/W2954537798","https://openalex.org/W3037578234","https://openalex.org/W3112727310","https://openalex.org/W4225580305","https://openalex.org/W4291033895","https://openalex.org/W4386702691","https://openalex.org/W4387828702","https://openalex.org/W6810798181"],"related_works":["https://openalex.org/W2112228652","https://openalex.org/W1908474219","https://openalex.org/W2545123933","https://openalex.org/W1994788526","https://openalex.org/W2041414401","https://openalex.org/W2108352898","https://openalex.org/W2612690400","https://openalex.org/W2042726296","https://openalex.org/W2160730947","https://openalex.org/W2081203575"],"abstract_inverted_index":{"Speckle":[0],"noise":[1,47],"significantly":[2],"affects":[3],"synthetic":[4],"aperture":[5],"radar":[6],"(SAR)":[7],"imaging":[8],"systems,":[9],"causing":[10],"difficulties":[11],"in":[12,146],"the":[13,46,58,72,77,103,107,114,120,130,134,140,143,153],"postprocessing":[14],"of":[15,109,123,133,142],"SAR":[16,148],"images.":[17],"To":[18],"strike":[19],"a":[20,63,96],"balance":[21],"between":[22],"denoising":[23],"performance":[24],"and":[25,128],"detail":[26],"retention,":[27],"this":[28,61],"study":[29],"proposes":[30],"an":[31,110],"innovative":[32],"deep":[33,97],"learning":[34],"despeckling":[35,150],"network":[36,42,92,115,145],"based":[37],"on":[38,119],"feature":[39,65,104],"supervision.":[40],"The":[41,136],"aims":[43],"to":[44,54,101,116],"enhance":[45],"reduction":[48],"capability":[49,74],"by":[50,75,84],"applying":[51],"regularized":[52],"supervision":[53],"shallow":[55],"features":[56,132],"within":[57],"network.":[59],"With":[60],"enhancement,":[62],"robust":[64],"extraction":[66],"module":[67,99],"(RFEM)":[68],"helps":[69],"further":[70,126],"improve":[71],"feature-capture":[73],"decomposing":[76],"image":[78,149,154],"into":[79],"different":[80,91],"frequency":[81],"components.":[82],"Subsequently,":[83],"integrating":[85],"complementary":[86],"prior":[87],"information":[88],"captured":[89],"from":[90],"architectures,":[93],"we":[94],"introduced":[95],"cross-denoising":[98],"(DCDM)":[100],"enrich":[102],"details.":[105,155],"Furthermore,":[106],"incorporation":[108],"attention":[111],"mechanism":[112],"enables":[113],"concentrate":[117],"more":[118],"information-rich":[121],"regions":[122],"interest,":[124],"thereby":[125],"refining":[127],"enhancing":[129],"detailed":[131],"image.":[135],"experimental":[137],"results":[138],"demonstrate":[139],"superiority":[141],"proposed":[144],"effective":[147],"while":[151],"preserving":[152]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}