{"id":"https://openalex.org/W2765116324","doi":"https://doi.org/10.1109/whispers.2015.8075429","title":"Active learning for hyperspectral image classification with a stacked autoencoders based neural network","display_name":"Active learning for hyperspectral image classification with a stacked autoencoders based neural network","publication_year":2015,"publication_date":"2015-06-01","ids":{"openalex":"https://openalex.org/W2765116324","doi":"https://doi.org/10.1109/whispers.2015.8075429","mag":"2765116324"},"language":"en","primary_location":{"id":"doi:10.1109/whispers.2015.8075429","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2015.8075429","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100679841","display_name":"Jiming Li","orcid":"https://orcid.org/0000-0002-5570-9952"},"institutions":[{"id":"https://openalex.org/I4210108177","display_name":"Zhejiang Police College","ror":"https://ror.org/01rxaf991","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210108177"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiming Li","raw_affiliation_strings":["Zhejiang Police college, Department of Forensic Science, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang Police college, Department of Forensic Science, Hangzhou, China","institution_ids":["https://openalex.org/I4210108177"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100679841"],"corresponding_institution_ids":["https://openalex.org/I4210108177"],"apc_list":null,"apc_paid":null,"fwci":0.5379,"has_fulltext":false,"cited_by_count":39,"citation_normalized_percentile":{"value":0.67574346,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"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":0.9958000183105469,"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":0.9958000183105469,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9957000017166138,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9787999987602234,"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/computer-science","display_name":"Computer science","score":0.7238835692405701},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7215771079063416},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.691391110420227},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6485315561294556},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.6188942193984985},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.590879499912262},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.566752552986145},{"id":"https://openalex.org/keywords/active-learning","display_name":"Active learning (machine learning)","score":0.5410115122795105},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.508120059967041},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47796937823295593},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4608791172504425},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.45929211378097534},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4287383258342743},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3862853944301605},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.344552218914032},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16997897624969482}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7238835692405701},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7215771079063416},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.691391110420227},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6485315561294556},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.6188942193984985},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.590879499912262},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.566752552986145},{"id":"https://openalex.org/C77967617","wikidata":"https://www.wikidata.org/wiki/Q4677561","display_name":"Active learning (machine learning)","level":2,"score":0.5410115122795105},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.508120059967041},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47796937823295593},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4608791172504425},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.45929211378097534},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4287383258342743},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3862853944301605},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.344552218914032},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16997897624969482},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0},{"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/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/whispers.2015.8075429","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2015.8075429","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2015 7th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/17","score":0.4000000059604645,"display_name":"Partnerships for the goals"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W1613249581","https://openalex.org/W2029296788","https://openalex.org/W2030476695","https://openalex.org/W2041478093","https://openalex.org/W2139573966","https://openalex.org/W2144966944","https://openalex.org/W2165049595","https://openalex.org/W6636456564"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2070598848","https://openalex.org/W3034375524","https://openalex.org/W2060875994","https://openalex.org/W2027399350","https://openalex.org/W2044184146","https://openalex.org/W2019190440","https://openalex.org/W2343470940","https://openalex.org/W3034864990"],"abstract_inverted_index":{"Active":[0],"learning":[1,20,31,57],"can":[2,39],"effectively":[3],"reduce":[4],"labelling":[5],"effort":[6],"for":[7,22,95],"remote":[8],"sensing":[9],"image":[10,24],"classification.":[11,25],"In":[12],"this":[13],"paper,":[14],"we":[15],"propose":[16],"a":[17,41,67,81,90,96],"new":[18],"active":[19,30,56,151],"method":[21,176,188],"hyperspectral":[23],"We":[26],"consider":[27],"batch":[28,152],"mode":[29],"and":[32,66,77,80,137,193],"relatively":[33],"large":[34],"amount":[35],"of":[36,47,110,126,162],"data":[37],"which":[38],"be":[40,132],"problem":[42],"when":[43],"using":[44],"current":[45,179],"state":[46],"the":[48,62,102,105,111,119,122,140,144,157,178],"art":[49],"algorithm":[50],"based":[51,60,184],"on":[52,61,74,169],"kernel":[53],"machines.":[54],"The":[55,114],"procedure":[58],"is":[59,99,147,165],"uncertainty":[63,121,130,186],"sampling":[64],"strategy":[65],"deep":[68,91],"neural":[69,92,112,145],"network.":[70,93,113],"Stacked":[71],"autoencoders":[72],"trained":[73],"redundant":[75],"spatial":[76],"spectral":[78],"features":[79],"few":[82],"labeled":[83],"training":[84,141],"samples":[85,127],"are":[86],"used":[87],"to":[88],"initialize":[89],"Uncertainty":[94],"given":[97],"sample":[98,123],"measured":[100],"by":[101],"difference":[103,116],"between":[104],"largest":[106],"two":[107],"class":[108],"outputs":[109],"less":[115],"there":[117],"is,":[118],"more":[120],"has.":[124],"Batch":[125],"with":[128],"most":[129],"will":[131,154],"selected":[133],"after":[134],"label":[135,163],"query":[136],"added":[138],"into":[139],"set.":[142],"Then":[143],"network":[146],"retrained.":[148],"And":[149],"such":[150],"selection":[153],"iterate":[155],"until":[156],"budget":[158],"(the":[159],"upper":[160],"limit":[161],"queries)":[164],"reached.":[166],"Experimental":[167],"results":[168],"Pavia":[170],"university":[171],"dataset":[172],"showed":[173],"that":[174],"our":[175],"outperforms":[177],"support":[180],"vector":[181],"machines":[182],"(SVMs)":[183],"multiclass/level":[185],"(MCLU)":[187],"both":[189],"in":[190],"classification":[191],"accuracy":[192],"generalization":[194],"capability.":[195]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":6},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":6},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
