{"id":"https://openalex.org/W2922095228","doi":"https://doi.org/10.1109/lgrs.2019.2901019","title":"Discriminative Feature Learning With Distance Constrained Stacked Sparse Autoencoder for Hyperspectral Target Detection","display_name":"Discriminative Feature Learning With Distance Constrained Stacked Sparse Autoencoder for Hyperspectral Target Detection","publication_year":2019,"publication_date":"2019-03-13","ids":{"openalex":"https://openalex.org/W2922095228","doi":"https://doi.org/10.1109/lgrs.2019.2901019","mag":"2922095228"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2019.2901019","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2019.2901019","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/A5009705011","display_name":"Yanzi Shi","orcid":"https://orcid.org/0000-0002-7717-985X"},"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":"Yanzi Shi","raw_affiliation_strings":["State Key Laboratory of Integrated Service Network, Xidian University, Xi\u2019an, China"],"raw_orcid":"https://orcid.org/0000-0002-7717-985X","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Service Network, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007285444","display_name":"Jie Lei","orcid":"https://orcid.org/0000-0003-0851-6565"},"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":"Jie Lei","raw_affiliation_strings":["State Key Laboratory of Integrated Service Network, Xidian University, Xi\u2019an, China"],"raw_orcid":"https://orcid.org/0000-0003-0851-6565","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Service Network, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101050599","display_name":"Yaping Yin","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":"Yaping Yin","raw_affiliation_strings":["State Key Laboratory of Integrated Service Network, Xidian University, Xi\u2019an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Service Network, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021612727","display_name":"Kailang Cao","orcid":"https://orcid.org/0000-0002-9093-2711"},"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":"Kailang Cao","raw_affiliation_strings":["State Key Laboratory of Integrated Service Network, Xidian University, Xi\u2019an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Service Network, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101862572","display_name":"Yunsong Li","orcid":"https://orcid.org/0000-0002-5239-6428"},"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":"Yunsong Li","raw_affiliation_strings":["State Key Laboratory of Integrated Service Network, Xidian University, Xi\u2019an, China"],"raw_orcid":"https://orcid.org/0000-0002-5239-6428","affiliations":[{"raw_affiliation_string":"State Key Laboratory of Integrated Service Network, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073412670","display_name":"Chein\u2010I Chang","orcid":"https://orcid.org/0000-0002-5450-4891"},"institutions":[{"id":"https://openalex.org/I75357094","display_name":"National Yunlin University of Science and Technology","ror":"https://ror.org/04qkq2m54","country_code":"TW","type":"education","lineage":["https://openalex.org/I75357094"]}],"countries":["TW"],"is_corresponding":false,"raw_author_name":"Chein-I Chang","raw_affiliation_strings":["Department of Computer Science & Information Engineering, National Yunlin University of Science & Technology, Douliu, Taiwan"],"raw_orcid":"https://orcid.org/0000-0002-5450-4891","affiliations":[{"raw_affiliation_string":"Department of Computer Science & Information Engineering, National Yunlin University of Science & Technology, Douliu, Taiwan","institution_ids":["https://openalex.org/I75357094"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":6,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.1682,"has_fulltext":false,"cited_by_count":51,"citation_normalized_percentile":{"value":0.94433083,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":100},"biblio":{"volume":"16","issue":"9","first_page":"1462","last_page":"1466"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998999834060669,"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":0.9998999834060669,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9901000261306763,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9797999858856201,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.8635324239730835},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8219822645187378},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7859607934951782},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7399437427520752},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7314043641090393},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6432949304580688},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5686687231063843},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4721398651599884},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.467875212430954},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.23779001832008362}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.8635324239730835},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8219822645187378},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7859607934951782},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7399437427520752},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7314043641090393},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6432949304580688},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5686687231063843},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4721398651599884},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.467875212430954},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.23779001832008362},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2019.2901019","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2019.2901019","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":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7300000190734863}],"awards":[{"id":"https://openalex.org/G1147426558","display_name":null,"funder_award_id":"JB180104","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G1424917938","display_name":null,"funder_award_id":"61701360","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G1595715893","display_name":null,"funder_award_id":"B08038","funder_id":"https://openalex.org/F4320327912","funder_display_name":"Higher Education Discipline Innovation Project"},{"id":"https://openalex.org/G4131587450","display_name":"\u56fe\u50cf\u7b97\u672f\u7f16\u7801\u7279\u5f81\u53ca\u540c\u6b65\u6280\u672f\u7814\u7a76","funder_award_id":"61571345","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G4507359911","display_name":null,"funder_award_id":"61502367","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5657849150","display_name":null,"funder_award_id":"91538101","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7389467270","display_name":null,"funder_award_id":"2017M620440","funder_id":"https://openalex.org/F4320321543","funder_display_name":"China Postdoctoral Science Foundation"},{"id":"https://openalex.org/G7594473715","display_name":null,"funder_award_id":"61801359","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G841248577","display_name":null,"funder_award_id":"61501346","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"},{"id":"https://openalex.org/F4320327912","display_name":"Higher Education Discipline Innovation Project","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":21,"referenced_works":["https://openalex.org/W625476304","https://openalex.org/W1532610010","https://openalex.org/W1899348529","https://openalex.org/W2017014096","https://openalex.org/W2040812261","https://openalex.org/W2062741922","https://openalex.org/W2087263574","https://openalex.org/W2110798204","https://openalex.org/W2156454229","https://openalex.org/W2163957348","https://openalex.org/W2422278448","https://openalex.org/W2533102868","https://openalex.org/W2754507318","https://openalex.org/W2765923665","https://openalex.org/W2789806937","https://openalex.org/W2799954862","https://openalex.org/W2884276099","https://openalex.org/W2888022311","https://openalex.org/W3099831940","https://openalex.org/W3105100264","https://openalex.org/W6676481782"],"related_works":["https://openalex.org/W2891059443","https://openalex.org/W2983142544","https://openalex.org/W4281663961","https://openalex.org/W3208888551","https://openalex.org/W4220682630","https://openalex.org/W4313561566","https://openalex.org/W3208386644","https://openalex.org/W3181622257","https://openalex.org/W3133533225","https://openalex.org/W3163146846"],"abstract_inverted_index":{"Target":[0],"detection":[1,53],"(TD)":[2],"is":[3,17,86,95,107,157],"one":[4],"of":[5,47],"the":[6,21,44,52,92,110,122,126,129,137,142,147,168,174,184],"major":[7],"tasks":[8],"in":[9,136,192],"hyperspectral":[10],"image":[11],"(HSI)":[12],"processing,":[13],"and":[14,66,132,179],"its":[15],"performance":[16],"greatly":[18],"affected":[19],"by":[20],"background.":[22],"Feature":[23],"extraction":[24],"(FE)":[25],"has":[26],"been":[27],"an":[28],"effective":[29],"way":[30],"to":[31,49,81,109,112],"mine":[32],"discriminative":[33,143],"information,":[34],"especially":[35,191],"FE":[36],"based":[37],"on":[38,163],"deep":[39,65,169],"learning,":[40],"which":[41,85],"can":[42,63],"learn":[43,64],"intrinsic":[45],"properties":[46],"data":[48],"further":[50],"improve":[51],"performance.":[54],"Unlike":[55],"supervised":[56,78],"networks,":[57],"unsupervised":[58],"stacked":[59],"sparse":[60],"autoencoders":[61],"(SSAEs)":[62],"nonlinear":[67],"features":[68,144,171],"without":[69],"any":[70],"labeled":[71],"data.":[72],"However,":[73],"SSAEs":[74],"usually":[75],"require":[76],"a":[77,103,114,149],"fine-tuned":[79],"model":[80],"obtain":[82],"better":[83],"discrimination,":[84],"not":[87],"feasible":[88],"for":[89,159],"TD,":[90],"since":[91],"prior":[93],"information":[94],"generally":[96],"insufficient.":[97],"In":[98],"this":[99],"letter,":[100],"we":[101],"introduce":[102],"distance":[104,116,123],"constraint":[105,124],"that":[106,167],"added":[108],"SSAE":[111,118],"form":[113],"new":[115],"constrained":[117],"(DCSSAE)":[119],"network.":[120],"Specifically,":[121],"maximizes":[125],"distinction":[127],"between":[128],"target":[130],"pixels":[131,135],"other":[133],"background":[134,160,193],"feature":[138],"space.":[139],"Then,":[140],"using":[141,152],"learned":[145,172],"from":[146,173],"DCSSAE,":[148],"simple":[150],"detector":[151],"radial":[153],"basis":[154],"function":[155],"kernel":[156],"derived":[158],"suppression.":[161,194],"Experiments":[162],"two":[164],"HSIs":[165],"demonstrate":[166],"spectral":[170],"DCSSAE":[175,185],"are":[176],"more":[177],"distinguishable,":[178],"our":[180],"proposed":[181],"detector,":[182,186],"namely,":[183],"outperforms":[187],"several":[188],"popular":[189],"detectors,":[190]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":5},{"year":2023,"cited_by_count":15},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}