{"id":"https://openalex.org/W2921213801","doi":"https://doi.org/10.1109/lgrs.2019.2939356","title":"Hyperspectral Image Classification With Deep Metric Learning and Conditional Random Field","display_name":"Hyperspectral Image Classification With Deep Metric Learning and Conditional Random Field","publication_year":2019,"publication_date":"2019-09-20","ids":{"openalex":"https://openalex.org/W2921213801","doi":"https://doi.org/10.1109/lgrs.2019.2939356","mag":"2921213801"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2019.2939356","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2019.2939356","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":["arxiv","crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1903.06258","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102004161","display_name":"Yi Liang","orcid":"https://orcid.org/0000-0001-6325-5906"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yi Liang","raw_affiliation_strings":["Center for Applied Mathematics, Tianjin University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Applied Mathematics, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057368785","display_name":"Xin Zhao","orcid":"https://orcid.org/0000-0002-1621-2337"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Zhao","raw_affiliation_strings":["Center for Applied Mathematics, Tianjin University, Tianjin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Applied Mathematics, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002301626","display_name":"Alan J. X. Guo","orcid":"https://orcid.org/0000-0002-9550-3633"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Alan J. X. Guo","raw_affiliation_strings":["Center for Applied Mathematics, Tianjin University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-9550-3633","affiliations":[{"raw_affiliation_string":"Center for Applied Mathematics, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049872589","display_name":"Fei Zhu","orcid":"https://orcid.org/0000-0002-8113-3707"},"institutions":[{"id":"https://openalex.org/I162868743","display_name":"Tianjin University","ror":"https://ror.org/012tb2g32","country_code":"CN","type":"education","lineage":["https://openalex.org/I162868743"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fei Zhu","raw_affiliation_strings":["Center for Applied Mathematics, Tianjin University, Tianjin, China"],"raw_orcid":"https://orcid.org/0000-0002-8113-3707","affiliations":[{"raw_affiliation_string":"Center for Applied Mathematics, Tianjin University, Tianjin, China","institution_ids":["https://openalex.org/I162868743"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I162868743"],"apc_list":null,"apc_paid":null,"fwci":2.0322,"has_fulltext":false,"cited_by_count":18,"citation_normalized_percentile":{"value":0.87241607,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":"17","issue":"6","first_page":"1042","last_page":"1046"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":1.0,"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":1.0,"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":0.994700014591217,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9440000057220459,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.7716416120529175},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.759990930557251},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7300667762756348},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.680099606513977},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6381233930587769},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.63592129945755},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5395646691322327},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.49976634979248047},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4583866000175476},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.42701074481010437},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.3736918866634369},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.10134163498878479}],"concepts":[{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.7716416120529175},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.759990930557251},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7300667762756348},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.680099606513977},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6381233930587769},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.63592129945755},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5395646691322327},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.49976634979248047},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4583866000175476},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.42701074481010437},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.3736918866634369},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.10134163498878479},{"id":"https://openalex.org/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/lgrs.2019.2939356","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2019.2939356","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"},{"id":"pmh:oai:arXiv.org:1903.06258","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1903.06258","pdf_url":"https://arxiv.org/pdf/1903.06258","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1903.06258","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1903.06258","pdf_url":"https://arxiv.org/pdf/1903.06258","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2482158783","display_name":null,"funder_award_id":"18JCQNJC01600","funder_id":"https://openalex.org/F4320323993","funder_display_name":"Natural Science Foundation of Tianjin City"},{"id":"https://openalex.org/G6269349810","display_name":null,"funder_award_id":"61701337","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/F4320323993","display_name":"Natural Science Foundation of Tianjin City","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W2001298023","https://openalex.org/W2004990382","https://openalex.org/W2029316659","https://openalex.org/W2052160904","https://openalex.org/W2090424610","https://openalex.org/W2097915756","https://openalex.org/W2103094532","https://openalex.org/W2136251662","https://openalex.org/W2144151128","https://openalex.org/W2147880316","https://openalex.org/W2151599207","https://openalex.org/W2155893237","https://openalex.org/W2161236525","https://openalex.org/W2162698522","https://openalex.org/W2168809519","https://openalex.org/W2500751094","https://openalex.org/W2520774990","https://openalex.org/W2547852346","https://openalex.org/W2602024454","https://openalex.org/W2732412926","https://openalex.org/W2783165089","https://openalex.org/W2794812819","https://openalex.org/W2800507189","https://openalex.org/W2805828795","https://openalex.org/W2808776742","https://openalex.org/W2811355488","https://openalex.org/W2889637463","https://openalex.org/W2894561647","https://openalex.org/W2899771611","https://openalex.org/W2950094539","https://openalex.org/W2952793010","https://openalex.org/W2963649946","https://openalex.org/W3099428178","https://openalex.org/W3100499011","https://openalex.org/W3101640299","https://openalex.org/W4240485910","https://openalex.org/W4295602020","https://openalex.org/W6682082992","https://openalex.org/W6726946684","https://openalex.org/W6751290860","https://openalex.org/W6756040250","https://openalex.org/W6785881002"],"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/W2044184146","https://openalex.org/W4313014865","https://openalex.org/W2019190440","https://openalex.org/W2343470940"],"abstract_inverted_index":{"To":[0],"improve":[1],"the":[2,6,24,29,43,47,63,71,83,117,121,126,132,136,140,152,157,163,169,183,186],"classification":[3,119,193],"performance":[4],"in":[5,94,189],"context":[7],"of":[8,31,49,171,185,191],"hyperspectral":[9],"image":[10,110],"(HSI)":[11],"processing,":[12],"many":[13],"works":[14],"have":[15],"been":[16],"developed":[17],"based":[18],"on":[19,178],"two":[20,179],"common":[21],"strategies,":[22],"namely,":[23],"spatial\u2013spectral":[25],"information":[26],"integration":[27],"and":[28,70,131,155,195],"utilization":[30],"neural":[32],"networks.":[33],"However,":[34],"both":[35,125,192],"strategies":[36],"typically":[37],"require":[38],"more":[39,92],"training":[40,172],"data":[41,173],"than":[42],"classical":[44],"algorithms,":[45],"aggregating":[46],"shortage":[48,170],"labeled":[50],"samples.":[51],"In":[52],"this":[53],"letter,":[54],"we":[55],"propose":[56],"a":[57],"novel":[58],"framework":[59,145],"that":[60,90],"organically":[61],"combines":[62],"spectrum-based":[64,88],"deep":[65],"metric":[66],"learning":[67],"(DML)":[68],"model":[69,79],"conditional":[72],"random":[73],"field":[74],"(CRF)":[75],"algorithm.":[76],"The":[77,99,143],"DML":[78,141,153],"is":[80,106,113,146],"supervised":[81],"by":[82,123,139,148],"center":[84],"loss":[85],"to":[86,115,174],"produce":[87],"features":[89,137,161],"gather":[91],"tightly":[93],"Euclidean":[95,133],"space":[96],"within":[97],"classes.":[98],"CRF":[100,164],"with":[101],"Gaussian":[102],"edge":[103],"potentials,":[104],"which":[105],"first":[107],"proposed":[108,144,187],"for":[109],"segmentation":[111],"tasks,":[112],"introduced":[114],"give":[116],"pixel-wise":[118],"over":[120],"HSI":[122],"utilizing":[124],"geographical":[127],"distances":[128,134],"between":[129,135],"pixels":[130,150],"produced":[138],"model.":[142],"trained":[147],"spectral":[149],"at":[151,162],"stage":[154],"utilizes":[156],"half":[158],"handcrafted":[159],"spatial":[160],"stage.":[165],"This":[166],"settlement":[167],"alleviates":[168],"some":[175],"extent.":[176],"Experiments":[177],"real":[180],"HSIs":[181],"demonstrate":[182],"advantages":[184],"method":[188],"terms":[190],"accuracy":[194],"computation":[196],"cost.":[197]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":1},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":1},{"year":2019,"cited_by_count":1}],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-10-10T00:00:00"}
