{"id":"https://openalex.org/W2106416367","doi":"https://doi.org/10.1109/igarss.2007.4423240","title":"A wavelet based targets detection method for high resolution airborne SAR data","display_name":"A wavelet based targets detection method for high resolution airborne SAR data","publication_year":2007,"publication_date":"2007-01-01","ids":{"openalex":"https://openalex.org/W2106416367","doi":"https://doi.org/10.1109/igarss.2007.4423240","mag":"2106416367"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2007.4423240","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2007.4423240","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2007 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/A5020900169","display_name":"Sirui Tian","orcid":"https://orcid.org/0000-0003-3601-6189"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210166112","display_name":"State Key Laboratory of Remote Sensing Science","ror":"https://ror.org/05wzjqa24","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210166112"]},{"id":"https://openalex.org/I25254941","display_name":"Beijing Normal University","ror":"https://ror.org/022k4wk35","country_code":"CN","type":"education","lineage":["https://openalex.org/I25254941"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Sirui Tian","raw_affiliation_strings":["China Remote Sensing Satellite Ground Station, Chinese Academy and Sciences, Beijing, China","Graduate School of Chinese Academy of Sciences, Beijing, China","State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences, Beijing Normal University, China"],"affiliations":[{"raw_affiliation_string":"China Remote Sensing Satellite Ground Station, Chinese Academy and Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210166112"]},{"raw_affiliation_string":"Graduate School of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I19820366"]},{"raw_affiliation_string":"State Key Laboratory of Remote Sensing Science, Jointly Sponsored by the Institute of Remote Sensing Applications of Chinese Academy of Sciences, Beijing Normal University, China","institution_ids":["https://openalex.org/I4210166112","https://openalex.org/I25254941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5115076700","display_name":"Chao Wang","orcid":"https://orcid.org/0000-0003-4887-923X"},"institutions":[{"id":"https://openalex.org/I4210166112","display_name":"State Key Laboratory of Remote Sensing Science","ror":"https://ror.org/05wzjqa24","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210166112"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Wang","raw_affiliation_strings":["China Remote Sensing Satellite Ground Station, Chinese Academy and Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"China Remote Sensing Satellite Ground Station, Chinese Academy and Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210166112"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100430255","display_name":"Hong Zhang","orcid":"https://orcid.org/0000-0002-0088-8148"},"institutions":[{"id":"https://openalex.org/I4210166112","display_name":"State Key Laboratory of Remote Sensing Science","ror":"https://ror.org/05wzjqa24","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210166112"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hong Zhang","raw_affiliation_strings":["China Remote Sensing Satellite Ground Station, Chinese Academy and Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"China Remote Sensing Satellite Ground Station, Chinese Academy and Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210166112"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101655125","display_name":"Bo Zhang","orcid":"https://orcid.org/0009-0005-3083-1547"},"institutions":[{"id":"https://openalex.org/I4210166112","display_name":"State Key Laboratory of Remote Sensing Science","ror":"https://ror.org/05wzjqa24","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210166112"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Bo Zhang","raw_affiliation_strings":["China Remote Sensing Satellite Ground Station, Chinese Academy and Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"China Remote Sensing Satellite Ground Station, Chinese Academy and Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210166112"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5113597664","display_name":"Fan Wu","orcid":"https://orcid.org/0000-0002-9280-8378"},"institutions":[{"id":"https://openalex.org/I4210166112","display_name":"State Key Laboratory of Remote Sensing Science","ror":"https://ror.org/05wzjqa24","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210166112"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fan Wu","raw_affiliation_strings":["China Remote Sensing Satellite Ground Station, Chinese Academy and Sciences, Beijing, China"],"affiliations":[{"raw_affiliation_string":"China Remote Sensing Satellite Ground Station, Chinese Academy and Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210166112"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5020900169"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I25254941","https://openalex.org/I4210166112"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.13904052,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"11","issue":null,"first_page":"2071","last_page":"2073"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.9998000264167786,"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":0.9998000264167786,"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/T10801","display_name":"Synthetic Aperture Radar (SAR) Applications and Techniques","score":0.9976999759674072,"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":0.9951000213623047,"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/wavelet","display_name":"Wavelet","score":0.7294546365737915},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7046563625335693},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7001701593399048},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.6810754537582397},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6318097114562988},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.6277729272842407},{"id":"https://openalex.org/keywords/synthetic-aperture-radar","display_name":"Synthetic aperture radar","score":0.5964308381080627},{"id":"https://openalex.org/keywords/speckle-pattern","display_name":"Speckle pattern","score":0.5546542406082153},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4726124107837677},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4494110345840454},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.43369171023368835},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.41770821809768677},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.0650017261505127}],"concepts":[{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.7294546365737915},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7046563625335693},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7001701593399048},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.6810754537582397},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6318097114562988},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.6277729272842407},{"id":"https://openalex.org/C87360688","wikidata":"https://www.wikidata.org/wiki/Q740686","display_name":"Synthetic aperture radar","level":2,"score":0.5964308381080627},{"id":"https://openalex.org/C102290492","wikidata":"https://www.wikidata.org/wiki/Q7575045","display_name":"Speckle pattern","level":2,"score":0.5546542406082153},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4726124107837677},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4494110345840454},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.43369171023368835},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.41770821809768677},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0650017261505127},{"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/igarss.2007.4423240","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2007.4423240","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2007 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":4,"referenced_works":["https://openalex.org/W2128033169","https://openalex.org/W2132984323","https://openalex.org/W2146842127","https://openalex.org/W2170719503"],"related_works":["https://openalex.org/W2793402697","https://openalex.org/W4391491570","https://openalex.org/W2042019967","https://openalex.org/W2093785611","https://openalex.org/W4386174346","https://openalex.org/W2157133621","https://openalex.org/W1993402303","https://openalex.org/W2008518276","https://openalex.org/W2077021924","https://openalex.org/W2152662390"],"abstract_inverted_index":{"A":[0],"wavelet":[1,57,64,76],"based":[2,28,65],"automatic":[3],"targets":[4],"detection":[5],"method":[6,26],"for":[7],"high":[8,160],"resolution":[9,40,161],"airborne":[10,162],"SAR":[11,163],"data":[12],"is":[13,27,44,80,122,137,156],"described":[14],"in":[15,86,108,143],"this":[16],"article":[17],"to":[18,70,104,125,129,139],"receive":[19],"faster":[20],"and":[21,62,165],"more":[22,45],"accuracy":[23],"detection.":[24,133],"This":[25],"on":[29],"the":[30,72,75,83,87,105,113,126,140,154,169],"assumption":[31],"that":[32,48,168],"man-made":[33],"objects":[34],"are":[35,119,147],"easily":[36],"detectable":[37],"at":[38],"low":[39],"because":[41],"their":[42],"scattering":[43],"persistent":[46],"than":[47],"of":[49,92,153],"natural":[50],"objects.":[51],"The":[52,134],"algorithm":[53,155,170],"involves":[54],"an":[55,99],"improved":[56,81],"soft":[58,77],"threshold":[59,78,101],"filter":[60,79],"(IWSTF)":[61],"a":[63,94,131],"RCCFAR":[66,114,117],"detector.":[67],"In":[68],"order":[69],"retain":[71],"target":[73],"feature,":[74],"by":[82,158,177],"strategy":[84],"used":[85],"enhanced":[88],"Lee":[89],"filter.":[90],"Instead":[91],"using":[93],"global":[95],"threshold,":[96],"we":[97],"adopted":[98],"adaptive":[100],"calculated":[102],"according":[103],"detail":[106],"coefficients":[107,128],"each":[109],"scale.":[110],"To":[111],"accelerate":[112],"detector,":[115],"two":[116],"detectors":[118],"used.":[120],"One":[121],"first":[123],"applied":[124,138],"approximate":[127],"make":[130],"coarse":[132],"other":[135],"one":[136],"filtered":[141],"images":[142],"those":[144],"regions":[145],"which":[146],"regarded":[148],"as":[149],"candidate":[150],"targets.":[151],"Performance":[152],"assessed":[157],"some":[159],"image":[164],"it":[166],"shows":[167],"can":[171],"effectively":[172],"reduce":[173],"false":[174],"alarms":[175],"caused":[176],"speckles.":[178]},"counts_by_year":[{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1},{"year":2016,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}