{"id":"https://openalex.org/W2124572229","doi":"https://doi.org/10.1109/igarss.2009.5417446","title":"Kernel regression-based background predicting method for target detection in SAR image","display_name":"Kernel regression-based background predicting method for target detection in SAR image","publication_year":2009,"publication_date":"2009-01-01","ids":{"openalex":"https://openalex.org/W2124572229","doi":"https://doi.org/10.1109/igarss.2009.5417446","mag":"2124572229"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2009.5417446","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2009.5417446","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE International Geoscience and Remote Sensing Symposium","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/A5034748006","display_name":"Yanfeng Gu","orcid":"https://orcid.org/0000-0003-1625-7989"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yanfeng Gu","raw_affiliation_strings":["College of Electronics and Information Engineering, Harbin Institute of Technology, China"],"affiliations":[{"raw_affiliation_string":"College of Electronics and Information Engineering, Harbin Institute of Technology, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100381479","display_name":"Xing Liu","orcid":"https://orcid.org/0000-0003-0814-8192"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xing Liu","raw_affiliation_strings":["College of Electronics and Information Engineering, Harbin Institute of Technology, China"],"affiliations":[{"raw_affiliation_string":"College of Electronics and Information Engineering, Harbin Institute of Technology, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5012456522","display_name":"Jinglong Han","orcid":"https://orcid.org/0000-0002-2465-5904"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinglong Han","raw_affiliation_strings":["College of Electronics and Information Engineering, Harbin Institute of Technology, China"],"affiliations":[{"raw_affiliation_string":"College of Electronics and Information Engineering, Harbin Institute of Technology, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100449291","display_name":"Ye Zhang","orcid":"https://orcid.org/0000-0001-8721-4535"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ye Zhang","raw_affiliation_strings":["College of Electronics and Information Engineering, Harbin Institute of Technology, China"],"affiliations":[{"raw_affiliation_string":"College of Electronics and Information Engineering, Harbin Institute of Technology, China","institution_ids":["https://openalex.org/I204983213"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5034748006"],"corresponding_institution_ids":["https://openalex.org/I204983213"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.1990586,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"IV","last_page":"593"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9983000159263611,"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"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9983000159263611,"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.998199999332428,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9961000084877014,"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/clutter","display_name":"Clutter","score":0.749469518661499},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7359440326690674},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6894634962081909},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.6345362663269043},{"id":"https://openalex.org/keywords/speckle-pattern","display_name":"Speckle pattern","score":0.6027630567550659},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.574141800403595},{"id":"https://openalex.org/keywords/constant-false-alarm-rate","display_name":"Constant false alarm rate","score":0.570820152759552},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5271639227867126},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.49746444821357727},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.4479255676269531},{"id":"https://openalex.org/keywords/speckle-noise","display_name":"Speckle noise","score":0.416031152009964},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.41360390186309814},{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.25470662117004395},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2147967517375946},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.12331104278564453}],"concepts":[{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.749469518661499},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7359440326690674},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6894634962081909},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.6345362663269043},{"id":"https://openalex.org/C102290492","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle pattern","level":2,"score":0.6027630567550659},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.574141800403595},{"id":"https://openalex.org/C77052588","wikidata":"https://www.wikidata.org/wiki/Q644307","display_name":"Constant false alarm rate","level":2,"score":0.570820152759552},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5271639227867126},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.49746444821357727},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.4479255676269531},{"id":"https://openalex.org/C180940675","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle noise","level":3,"score":0.416031152009964},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.41360390186309814},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.25470662117004395},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2147967517375946},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12331104278564453},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2009.5417446","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2009.5417446","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2009 IEEE International Geoscience and Remote Sensing Symposium","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":5,"referenced_works":["https://openalex.org/W2006262236","https://openalex.org/W2107517560","https://openalex.org/W2160445017","https://openalex.org/W2165447611","https://openalex.org/W4238717354"],"related_works":["https://openalex.org/W2388326001","https://openalex.org/W2115015615","https://openalex.org/W2351401443","https://openalex.org/W3011102797","https://openalex.org/W4320921117","https://openalex.org/W2431104759","https://openalex.org/W1649651896","https://openalex.org/W2317958419","https://openalex.org/W2116961228","https://openalex.org/W3173283069"],"abstract_inverted_index":{"Target":[0],"detection":[1,26,44,88],"with":[2,45],"SAR":[3,28,46,95,101],"image":[4,47,58,102],"is":[5,22,59,89],"one":[6],"of":[7,82,120],"important":[8],"research":[9],"topics":[10],"in":[11,27,71],"remote":[12],"sensing.":[13],"In":[14,49],"this":[15],"paper,":[16],"a":[17],"kernel":[18,54,77],"regression-based":[19,78],"predicting":[20],"method":[21],"proposed":[23,51,106],"for":[24],"target":[25,43],"image.":[29,96],"Badly":[30],"speckle":[31],"noise":[32],"and":[33,67,80,111,115],"background":[34,65,83,93,113],"clutter":[35],"are":[36],"two":[37],"main":[38],"factors":[39],"which":[40],"make":[41,68],"the":[42,50,53,64,92,105,118,121],"difficult.":[48],"method,":[52],"regression":[55],"on":[56,91,99],"local":[57],"used":[60],"to":[61],"exactly":[62],"predict":[63,110],"interferences":[66],"Gaussian":[69],"assumption":[70],"conventional":[72,122],"detector":[73],"better":[74],"followed":[75],"after":[76],"prediction":[79],"suppression":[81],"clutter.":[84],"Thus,":[85],"final":[86],"CFAR":[87,123],"performed":[90],"clutter-removed":[94],"Experiments":[97],"conducted":[98],"real":[100],"show":[103],"that":[104],"algorithm":[107],"can":[108],"effectively":[109],"suppress":[112],"clutters,":[114],"greatly":[116],"improve":[117],"performance":[119],"detector.":[124]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
