{"id":"https://openalex.org/W3130123246","doi":"https://doi.org/10.1109/igarss39084.2020.9324302","title":"Training Capsnets via Active Learning for Hyperspectral Image Classification","display_name":"Training Capsnets via Active Learning for Hyperspectral Image Classification","publication_year":2020,"publication_date":"2020-09-26","ids":{"openalex":"https://openalex.org/W3130123246","doi":"https://doi.org/10.1109/igarss39084.2020.9324302","mag":"3130123246"},"language":"en","primary_location":{"id":"doi:10.1109/igarss39084.2020.9324302","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9324302","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-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/A5046123228","display_name":"Mercedes E. Paoletti","orcid":"https://orcid.org/0000-0003-1030-3729"},"institutions":[{"id":"https://openalex.org/I80606768","display_name":"Universidad de Extremadura","ror":"https://ror.org/0174shg90","country_code":"ES","type":"education","lineage":["https://openalex.org/I80606768"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Mercedes E. Paoletti","raw_affiliation_strings":["Hyperspectral Computing Laboratory, Escuela Polit\u00e9cnica, University of Extremadura, C\u00e1ceres, Spain"],"affiliations":[{"raw_affiliation_string":"Hyperspectral Computing Laboratory, Escuela Polit\u00e9cnica, University of Extremadura, C\u00e1ceres, Spain","institution_ids":["https://openalex.org/I80606768"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039673511","display_name":"Juan M. Haut","orcid":"https://orcid.org/0000-0001-6701-961X"},"institutions":[{"id":"https://openalex.org/I80606768","display_name":"Universidad de Extremadura","ror":"https://ror.org/0174shg90","country_code":"ES","type":"education","lineage":["https://openalex.org/I80606768"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Juan M. Haut","raw_affiliation_strings":["Hyperspectral Computing Laboratory, Escuela Polit\u00e9cnica, University of Extremadura, C\u00e1ceres, Spain"],"affiliations":[{"raw_affiliation_string":"Hyperspectral Computing Laboratory, Escuela Polit\u00e9cnica, University of Extremadura, C\u00e1ceres, Spain","institution_ids":["https://openalex.org/I80606768"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010624980","display_name":"Javier Plaza","orcid":"https://orcid.org/0000-0002-2384-9141"},"institutions":[{"id":"https://openalex.org/I80606768","display_name":"Universidad de Extremadura","ror":"https://ror.org/0174shg90","country_code":"ES","type":"education","lineage":["https://openalex.org/I80606768"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Javier Plaza","raw_affiliation_strings":["Hyperspectral Computing Laboratory, Escuela Polit\u00e9cnica, University of Extremadura, C\u00e1ceres, Spain"],"affiliations":[{"raw_affiliation_string":"Hyperspectral Computing Laboratory, Escuela Polit\u00e9cnica, University of Extremadura, C\u00e1ceres, Spain","institution_ids":["https://openalex.org/I80606768"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054292278","display_name":"Antonio Plaza","orcid":"https://orcid.org/0000-0002-9613-1659"},"institutions":[{"id":"https://openalex.org/I80606768","display_name":"Universidad de Extremadura","ror":"https://ror.org/0174shg90","country_code":"ES","type":"education","lineage":["https://openalex.org/I80606768"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Antonio Plaza","raw_affiliation_strings":["Hyperspectral Computing Laboratory, Escuela Polit\u00e9cnica, University of Extremadura, C\u00e1ceres, Spain"],"affiliations":[{"raw_affiliation_string":"Hyperspectral Computing Laboratory, Escuela Polit\u00e9cnica, University of Extremadura, C\u00e1ceres, Spain","institution_ids":["https://openalex.org/I80606768"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5046123228"],"corresponding_institution_ids":["https://openalex.org/I80606768"],"apc_list":null,"apc_paid":null,"fwci":0.9433,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.82428239,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"40","last_page":"43"},"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9810000061988831,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"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/T10057","display_name":"Face and Expression Recognition","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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8057277202606201},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7669188380241394},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7299181222915649},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6505303382873535},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6096639633178711},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5663661360740662},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.424209326505661},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.4239957332611084},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3335859179496765},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.28251487016677856}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8057277202606201},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7669188380241394},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7299181222915649},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6505303382873535},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6096639633178711},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5663661360740662},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.424209326505661},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.4239957332611084},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3335859179496765},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.28251487016677856}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss39084.2020.9324302","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss39084.2020.9324302","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2875449310","display_name":null,"funder_award_id":"FPU15/02090","funder_id":"https://openalex.org/F4320322930","funder_display_name":"Ministerio de Ciencia e Innovaci\u00f3n"},{"id":"https://openalex.org/G5288112272","display_name":null,"funder_award_id":"GR18060,734541-EXPOSURE","funder_id":"https://openalex.org/F4320321595","funder_display_name":"Federaci\u00f3n Espa\u00f1ola de Enfermedades Raras"}],"funders":[{"id":"https://openalex.org/F4320321595","display_name":"Federaci\u00f3n Espa\u00f1ola de Enfermedades Raras","ror":"https://ror.org/0348bpk17"},{"id":"https://openalex.org/F4320322930","display_name":"Ministerio de Ciencia e Innovaci\u00f3n","ror":"https://ror.org/034900433"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":8,"referenced_works":["https://openalex.org/W2010797000","https://openalex.org/W2101365302","https://openalex.org/W2772452219","https://openalex.org/W2809113079","https://openalex.org/W2898381489","https://openalex.org/W2963703618","https://openalex.org/W2991616716","https://openalex.org/W6743446608"],"related_works":["https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3133861977","https://openalex.org/W2951211570","https://openalex.org/W3103566983","https://openalex.org/W3167935049","https://openalex.org/W3029198973","https://openalex.org/W2952813363","https://openalex.org/W4360783045","https://openalex.org/W2963346891"],"abstract_inverted_index":{"Hyperspectral":[0],"imaging":[1],"(HSI)":[2],"gathers":[3],"hundreds":[4],"of":[5,18,25,56,98,107,133,175,182,216],"images":[6],"along":[7],"the":[8,12,16,19,54,64,82,92,96,103,108,130,153,159,167,201,204,209,214],"electromagnetic":[9],"spectrum":[10],"for":[11,94,195],"same":[13],"area":[14],"on":[15,102,144],"surface":[17],"Earth,":[20],"collecting":[21],"a":[22,74,172,180,191],"rich":[23],"amount":[24],"spatial":[26,205],"and":[27,68,105,119,127,129,203,212],"spectral":[28,202],"information.":[29],"Deep":[30],"learning":[31,86,163],"classifiers":[32,142],"have":[33],"achieved":[34],"significantly":[35],"high":[36,131],"precision":[37],"results":[38],"when":[39,218],"analyzing":[40],"HSI":[41,114,134,160,196,210],"data.":[42,161],"In":[43],"particular,":[44],"capsule":[45],"networks":[46,60],"(CapsNets)":[47],"can":[48,165],"provide":[49],"robust":[50],"classification":[51,198],"results,":[52],"overcoming":[53],"limitations":[55],"traditional":[57],"convolutional":[58],"neural":[59],"(CNNs)":[61],"by":[62,170],"enriching":[63],"feature":[65],"presentation":[66],"capability":[67],"applying":[69],"dynamic":[70],"routing":[71],"mechanisms.":[72],"As":[73],"result,":[75],"CapsNets":[76,99,217],"are":[77,223],"now":[78],"widely":[79],"regarded":[80],"as":[81,89],"state-of-the-art":[83],"within":[84],"deep":[85],"field.":[87],"However,":[88],"it":[90,137],"is":[91,125,149],"case":[93],"CNNs,":[95],"performance":[97,215],"strongly":[100],"depends":[101],"quantity":[104],"quality":[106],"available":[109],"training":[110,146,221],"samples,":[111],"which":[112],"in":[113,158,185,208],"tends":[115],"to":[116,139,152],"be":[117],"scarce":[118],"noisy.":[120],"Moreover,":[121],"obtaining":[122],"labeled":[123,177],"data":[124,135,197,211],"expensive":[126],"time-consuming,":[128],"dimensionality":[132],"makes":[136],"difficult":[138],"accurately":[140],"design":[141],"based":[143],"limited":[145,220],"samples.":[147],"This":[148,188],"mainly":[150],"due":[151],"strong":[154],"intra-class":[155],"variability":[156],"present":[157],"Active":[162],"(AL)":[164],"alleviate":[166],"aforementioned":[168],"problems":[169],"selecting":[171],"small":[173],"set":[174],"highly-representative":[176],"samples":[178,222],"from":[179],"pool":[181],"unlabeled":[183],"data,":[184],"iterative":[186],"fashion.":[187],"paper":[189],"presents":[190],"new":[192],"AL-based":[193],"approach":[194],"that":[199],"integrates":[200],"information":[206],"contained":[207],"enhances":[213],"very":[219],"available.":[224],"Code:":[225],"https://github.com/mhaut/AL-CapsNet-HSI.":[226]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
