{"id":"https://openalex.org/W3157052017","doi":"https://doi.org/10.1109/lgrs.2021.3072249","title":"Fractional Fourier Transform and Transferred CNN Based on Tensor for Hyperspectral Anomaly Detection","display_name":"Fractional Fourier Transform and Transferred CNN Based on Tensor for Hyperspectral Anomaly Detection","publication_year":2021,"publication_date":"2021-04-21","ids":{"openalex":"https://openalex.org/W3157052017","doi":"https://doi.org/10.1109/lgrs.2021.3072249","mag":"3157052017"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2021.3072249","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2021.3072249","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/A5026198068","display_name":"Lili Zhang","orcid":"https://orcid.org/0000-0003-0547-3803"},"institutions":[{"id":"https://openalex.org/I4210155943","display_name":"Daqing Normal University","ror":"https://ror.org/05dd1f546","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210155943"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lili Zhang","raw_affiliation_strings":["College of Mechanical and Electrical Engineering, Daqing Normal University, Daqing, China"],"raw_orcid":"https://orcid.org/0000-0003-0547-3803","affiliations":[{"raw_affiliation_string":"College of Mechanical and Electrical Engineering, Daqing Normal University, Daqing, China","institution_ids":["https://openalex.org/I4210155943"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085798243","display_name":"Baozhi Cheng","orcid":null},"institutions":[{"id":"https://openalex.org/I4210155943","display_name":"Daqing Normal University","ror":"https://ror.org/05dd1f546","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210155943"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baozhi Cheng","raw_affiliation_strings":["College of Mechanical and Electrical Engineering, Daqing Normal University, Daqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"College of Mechanical and Electrical Engineering, Daqing Normal University, Daqing, China","institution_ids":["https://openalex.org/I4210155943"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210155943"],"apc_list":{"value":2045,"currency":"USD","value_usd":2045},"apc_paid":null,"fwci":2.2984,"has_fulltext":false,"cited_by_count":30,"citation_normalized_percentile":{"value":0.88545547,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":99},"biblio":{"volume":"19","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998000264167786,"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":0.9998000264167786,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9991999864578247,"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.9177595376968384},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6311987638473511},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6163758039474487},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.5924625992774963},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.572594165802002},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5560994148254395},{"id":"https://openalex.org/keywords/tensor","display_name":"Tensor (intrinsic definition)","score":0.5361955165863037},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5182073712348938},{"id":"https://openalex.org/keywords/fourier-transform","display_name":"Fourier transform","score":0.5157747268676758},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4407254755496979},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.43650007247924805},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.41289815306663513},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.40304839611053467},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.36730533838272095},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.1056259274482727}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9177595376968384},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6311987638473511},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6163758039474487},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.5924625992774963},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.572594165802002},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5560994148254395},{"id":"https://openalex.org/C155281189","wikidata":"https://www.wikidata.org/wiki/Q3518150","display_name":"Tensor (intrinsic definition)","level":2,"score":0.5361955165863037},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5182073712348938},{"id":"https://openalex.org/C102519508","wikidata":"https://www.wikidata.org/wiki/Q6520159","display_name":"Fourier transform","level":2,"score":0.5157747268676758},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4407254755496979},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.43650007247924805},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.41289815306663513},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.40304839611053467},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.36730533838272095},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.1056259274482727},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2021.3072249","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2021.3072249","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":[{"score":0.5699999928474426,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"},{"score":0.41999998688697815,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[{"id":"https://openalex.org/G5976730603","display_name":null,"funder_award_id":"61901082","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8735947216","display_name":null,"funder_award_id":"LH2019F001","funder_id":"https://openalex.org/F4320323085","funder_display_name":"Natural Science Foundation of Heilongjiang Province"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320323085","display_name":"Natural Science Foundation of Heilongjiang Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W2004491663","https://openalex.org/W2017014096","https://openalex.org/W2047870694","https://openalex.org/W2086506050","https://openalex.org/W2124463804","https://openalex.org/W2145096794","https://openalex.org/W2158340226","https://openalex.org/W2163129097","https://openalex.org/W2165447611","https://openalex.org/W2288752886","https://openalex.org/W2424277038","https://openalex.org/W2497075055","https://openalex.org/W2518897583","https://openalex.org/W2592141703","https://openalex.org/W2782930397","https://openalex.org/W2884276099","https://openalex.org/W2901555355","https://openalex.org/W2959891261","https://openalex.org/W2975506318","https://openalex.org/W2999536074","https://openalex.org/W3033043020","https://openalex.org/W3091231798","https://openalex.org/W3097141235","https://openalex.org/W3099831940","https://openalex.org/W3110074314","https://openalex.org/W3111681251"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2070598848","https://openalex.org/W2404757046","https://openalex.org/W2044184146","https://openalex.org/W4313014865","https://openalex.org/W2184115898"],"abstract_inverted_index":{"Most":[0],"of":[1,66,109,173],"the":[2,12,63,92,107,113,125,129,141,149,160,171,174],"algorithms":[3,26],"for":[4,59,159],"hyperspectral":[5,60,71,167],"anomaly":[6],"detection":[7],"(AD)":[8],"are":[9],"based":[10,27,53,153],"on":[11,28,54,154,165],"original":[13],"spectral":[14,110],"signatures":[15],"which":[16,138],"may":[17,139],"suffer":[18],"noise":[19,127],"contamination.":[20],"In":[21,38],"recent":[22],"years,":[23],"some":[24],"AD":[25],"deep":[29],"learning":[30],"(DL)":[31],"and":[32,48,79,124,145],"tensor":[33,55,155],"have":[34],"achieved":[35],"satisfactory":[36],"results.":[37,162],"this":[39],"letter,":[40],"an":[41],"algorithm":[42],"using":[43],"fractional":[44,130],"Fourier":[45,131],"transform":[46],"(FrFT)":[47],"transferred":[49,151],"convolutional":[50],"neural":[51],"network":[52],"(FrFTTCNNT)":[56],"is":[57,74,89,97,120,157],"proposed":[58,175],"AD.":[61],"First,":[62],"test":[64,68],"block":[65],"each":[67],"point":[69],"in":[70,128,148],"imagery":[72],"(HSI)":[73],"transformed":[75,121],"into":[76],"1-D":[77],"vector":[78],"a":[80],"higher":[81,93],"dimensional":[82,94,115],"data":[83,95,116,168],"set":[84,96,117],"with":[85],"more":[86],"spatial":[87],"information":[88],"obtained.":[90],"Furthermore,":[91],"dimensionally":[98],"reduced":[99],"by":[100,122],"principal":[101],"component":[102],"analysis":[103],"(PCA)":[104],"to":[105],"remove":[106],"redundancy":[108],"bands.":[111],"Then,":[112],"lower":[114],"after":[118],"PCA":[119],"FrFT":[123],"nonstationary":[126],"domain":[132],"(FrFD)":[133],"can":[134],"be":[135],"better":[136],"suppressed":[137],"increase":[140],"discrimination":[142],"between":[143],"background":[144],"targets.":[146],"Finally,":[147],"FrFD,":[150],"CNN":[152],"(TCNNT)":[156],"employed":[158],"final":[161],"Experiments":[163],"conducted":[164],"three":[166],"sets":[169],"show":[170],"superiority":[172],"FrFTTCNNT.":[176]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":2}],"updated_date":"2026-08-28T12:50:07.497085","created_date":"2025-10-10T00:00:00"}
