{"id":"https://openalex.org/W2953960221","doi":"https://doi.org/10.1109/whispers.2018.8747033","title":"Iterative active learning with diffusion geometry for hyperspectral images","display_name":"Iterative active learning with diffusion geometry for hyperspectral images","publication_year":2018,"publication_date":"2018-09-01","ids":{"openalex":"https://openalex.org/W2953960221","doi":"https://doi.org/10.1109/whispers.2018.8747033","mag":"2953960221"},"language":"en","primary_location":{"id":"doi:10.1109/whispers.2018.8747033","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2018.8747033","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 9th 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":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5022923838","display_name":"James M. Murphy","orcid":"https://orcid.org/0000-0001-6598-044X"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"James M. Murphy","raw_affiliation_strings":["Department of Mathematics, Johns Hopkins University"],"affiliations":[{"raw_affiliation_string":"Department of Mathematics, Johns Hopkins University","institution_ids":["https://openalex.org/I145311948"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5089371996","display_name":"Mauro Maggioni","orcid":"https://orcid.org/0000-0003-3258-9297"},"institutions":[{"id":"https://openalex.org/I145311948","display_name":"Johns Hopkins University","ror":"https://ror.org/00za53h95","country_code":"US","type":"education","lineage":["https://openalex.org/I145311948"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mauro Maggioni","raw_affiliation_strings":["Department of Applied Mathematics and Statistics, Johns Hopkins University"],"affiliations":[{"raw_affiliation_string":"Department of Applied Mathematics and Statistics, Johns Hopkins University","institution_ids":["https://openalex.org/I145311948"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5022923838"],"corresponding_institution_ids":["https://openalex.org/I145311948"],"apc_list":null,"apc_paid":null,"fwci":1.4602,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.86407767,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9983000159263611,"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":0.9983000159263611,"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":0.9901999831199646,"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/T12946","display_name":"Fractal and DNA sequence analysis","score":0.9520999789237976,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.9427037239074707},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.8867922425270081},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6834993362426758},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6136926412582397},{"id":"https://openalex.org/keywords/ground-truth","display_name":"Ground truth","score":0.6038259863853455},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.544529378414154},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.48814353346824646},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.4816919267177582},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.45576438307762146},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.44282451272010803},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.4373796582221985},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.416394978761673},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3672020435333252}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9427037239074707},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.8867922425270081},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6834993362426758},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6136926412582397},{"id":"https://openalex.org/C146849305","wikidata":"https://www.wikidata.org/wiki/Q370766","display_name":"Ground truth","level":2,"score":0.6038259863853455},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.544529378414154},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.48814353346824646},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.4816919267177582},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.45576438307762146},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.44282451272010803},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.4373796582221985},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.416394978761673},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3672020435333252},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C43617362","wikidata":"https://www.wikidata.org/wiki/Q170050","display_name":"Chromatography","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/whispers.2018.8747033","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2018.8747033","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 9th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W571150846","https://openalex.org/W1480376833","https://openalex.org/W2006554089","https://openalex.org/W2018482939","https://openalex.org/W2062822804","https://openalex.org/W2111843353","https://openalex.org/W2127816222","https://openalex.org/W2133296809","https://openalex.org/W2137965886","https://openalex.org/W2153409933","https://openalex.org/W2333549353","https://openalex.org/W2342626288","https://openalex.org/W2607508964","https://openalex.org/W2767184555","https://openalex.org/W2775089166","https://openalex.org/W2794954404","https://openalex.org/W2799770756","https://openalex.org/W2895913707","https://openalex.org/W2990418500","https://openalex.org/W3099394812","https://openalex.org/W3105730638","https://openalex.org/W4213367101","https://openalex.org/W4300420063","https://openalex.org/W6678881507","https://openalex.org/W6680203652","https://openalex.org/W6746524505","https://openalex.org/W6755172055"],"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/W2901421464","https://openalex.org/W2932657337"],"abstract_inverted_index":{"We":[0],"propose":[1],"an":[2],"active":[3,86],"learning":[4,83,87],"algorithm":[5],"for":[6],"labeling":[7,78],"hyperspectral":[8],"images":[9],"(HSI).":[10],"Pixels":[11],"with":[12,32,91],"ambiguous":[13],"class":[14],"affinity":[15],"are":[16,29,46],"iteratively":[17],"estimated":[18],"using":[19],"geometric":[20],"and":[21,84],"statistical":[22],"properties":[23],"of":[24,42,63],"the":[25,51,61],"data.":[26,53],"These":[27],"pixels":[28],"then":[30],"labeled":[31,43],"ground":[33],"truth":[34],"data,":[35],"yielding":[36],"a":[37,93],"small":[38],"but":[39],"potent":[40],"set":[41],"pixels,":[44,65],"which":[45],"consequently":[47],"used":[48],"to":[49,81],"label":[50,96],"remaining":[52],"The":[54],"proposed":[55],"method":[56],"enjoys":[57],"quasilinear":[58],"complexity":[59],"in":[60,77],"number":[62],"sample":[64],"as":[66,68],"well":[67],"competitive":[69],"empirical":[70],"performance":[71],"on":[72],"real":[73],"HSI.":[74],"Substantial":[75],"improvement":[76],"accuracy":[79],"compared":[80],"unsupervised":[82],"existing":[85],"methods":[88],"is":[89],"observed":[90],"just":[92],"few":[94],"well-selected":[95],"queries.":[97]},"counts_by_year":[{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2026-03-25T13:04:00.132906","created_date":"2025-10-10T00:00:00"}