{"id":"https://openalex.org/W2765876051","doi":"https://doi.org/10.1109/whispers.2014.8077529","title":"Combining active and metric learning for hyperspectral image classification","display_name":"Combining active and metric learning for hyperspectral image classification","publication_year":2014,"publication_date":"2014-06-01","ids":{"openalex":"https://openalex.org/W2765876051","doi":"https://doi.org/10.1109/whispers.2014.8077529","mag":"2765876051"},"language":"en","primary_location":{"id":"doi:10.1109/whispers.2014.8077529","is_oa":true,"landing_page_url":"https://doi.org/10.1109/whispers.2014.8077529","pdf_url":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8077529","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8077529","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5075665406","display_name":"Edoardo Pasolli","orcid":"https://orcid.org/0000-0003-0799-3490"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Edoardo Pasolli","raw_affiliation_strings":["School of Civil Engineering, Purdue University, West Lafayette, IN"],"affiliations":[{"raw_affiliation_string":"School of Civil Engineering, Purdue University, West Lafayette, IN","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024089585","display_name":"H. Lexie Yang","orcid":"https://orcid.org/0000-0003-2252-6778"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hsiuhan Lexie Yang","raw_affiliation_strings":["School of Civil Engineering, Purdue University, West Lafayette, IN"],"affiliations":[{"raw_affiliation_string":"School of Civil Engineering, Purdue University, West Lafayette, IN","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002083374","display_name":"Melba M. Crawford","orcid":null},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Melba M. Crawford","raw_affiliation_strings":["School of Civil Engineering, Purdue University, West Lafayette, IN"],"affiliations":[{"raw_affiliation_string":"School of Civil Engineering, Purdue University, West Lafayette, IN","institution_ids":["https://openalex.org/I219193219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5075665406"],"corresponding_institution_ids":["https://openalex.org/I219193219"],"apc_list":null,"apc_paid":null,"fwci":0.4481,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.75430199,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"290","issue":null,"first_page":"1","last_page":"4"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9984999895095825,"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":0.9984999895095825,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9847000241279602,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9810000061988831,"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/large-margin-nearest-neighbor","display_name":"Large margin nearest neighbor","score":0.8641493320465088},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7791807651519775},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7142737507820129},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6834784746170044},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.6768798828125},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6613290905952454},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.6526097059249878},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6131426692008972},{"id":"https://openalex.org/keywords/k-nearest-neighbors-algorithm","display_name":"k-nearest neighbors algorithm","score":0.6067237257957458},{"id":"https://openalex.org/keywords/nonlinear-dimensionality-reduction","display_name":"Nonlinear dimensionality reduction","score":0.5673006772994995},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.545659065246582},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5182616710662842},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4735640287399292},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.4725888669490814},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38504356145858765}],"concepts":[{"id":"https://openalex.org/C94475309","wikidata":"https://www.wikidata.org/wiki/Q6489154","display_name":"Large margin nearest neighbor","level":3,"score":0.8641493320465088},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7791807651519775},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7142737507820129},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6834784746170044},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.6768798828125},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6613290905952454},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.6526097059249878},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6131426692008972},{"id":"https://openalex.org/C113238511","wikidata":"https://www.wikidata.org/wiki/Q1071612","display_name":"k-nearest neighbors algorithm","level":2,"score":0.6067237257957458},{"id":"https://openalex.org/C151876577","wikidata":"https://www.wikidata.org/wiki/Q7049464","display_name":"Nonlinear dimensionality reduction","level":3,"score":0.5673006772994995},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.545659065246582},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5182616710662842},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4735640287399292},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.4725888669490814},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38504356145858765},{"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":1,"locations":[{"id":"doi:10.1109/whispers.2014.8077529","is_oa":true,"landing_page_url":"https://doi.org/10.1109/whispers.2014.8077529","pdf_url":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8077529","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1109/whispers.2014.8077529","is_oa":true,"landing_page_url":"https://doi.org/10.1109/whispers.2014.8077529","pdf_url":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8077529","source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 6th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.44999998807907104,"display_name":"Quality Education","id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2765876051.pdf","grobid_xml":"https://content.openalex.org/works/W2765876051.grobid-xml"},"referenced_works_count":14,"referenced_works":["https://openalex.org/W84941271","https://openalex.org/W1981823412","https://openalex.org/W2001141328","https://openalex.org/W2025263547","https://openalex.org/W2038911891","https://openalex.org/W2059497048","https://openalex.org/W2106053110","https://openalex.org/W2107131609","https://openalex.org/W2132525626","https://openalex.org/W2136261952","https://openalex.org/W2151599207","https://openalex.org/W3148981562","https://openalex.org/W6665487591","https://openalex.org/W6680332746"],"related_works":["https://openalex.org/W2375574759","https://openalex.org/W2383239174","https://openalex.org/W3088634662","https://openalex.org/W117517268","https://openalex.org/W3162910294","https://openalex.org/W2539700568","https://openalex.org/W2931531042","https://openalex.org/W1489327846","https://openalex.org/W4287375746","https://openalex.org/W3124275785"],"abstract_inverted_index":{"Classification":[0],"of":[1,14,21,93,125],"hyperspectral":[2,120],"remote":[3],"sensing":[4],"images":[5],"is":[6,78,98,114],"affected":[7],"by":[8,80],"two":[9,33],"main":[10],"problems:":[11],"high":[12],"dimensionality":[13],"the":[15,40,123,126],"acquired":[16],"signatures":[17],"and":[18,29,66],"scarce":[19],"availability":[20],"labeled":[22,94],"samples.":[23],"Learning":[24],"a":[25,60,73,86,109,118],"low":[26],"dimensional":[27],"manifold":[28],"active":[30,67],"learning":[31,68,88],"represent":[32],"approaches":[34],"that":[35,90],"have":[36],"been":[37],"investigated":[38],"in":[39,62,100],"literature":[41],"to":[42],"mitigate":[43],"these":[44],"effects.":[45],"However":[46],"they":[47],"are":[48,69],"usually":[49],"applied":[50,99],"independently":[51],"from":[52],"each":[53],"other.":[54],"In":[55,71],"this":[56],"paper":[57],"we":[58],"propose":[59],"method":[61,97],"which":[63,108],"feature":[64,76],"extraction":[65],"combined.":[70],"particular,":[72],"new":[74,110],"reduced":[75],"space":[77],"learned":[79],"large":[81],"margin":[82],"nearest":[83],"neighbor":[84,104],"(LMNN),":[85],"metric":[87],"strategy":[89,113],"takes":[91],"advantage":[92],"information.":[95],"The":[96],"conjunction":[101],"with":[102],"k-nearest":[103],"(k-NN)":[105],"classification,":[106],"for":[107],"sample":[111],"selection":[112],"proposed.":[115],"Experiments":[116],"on":[117],"real":[119],"dataset":[121],"confirm":[122],"effectiveness":[124],"proposed":[127],"method.":[128]},"counts_by_year":[{"year":2015,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
