{"id":"https://openalex.org/W2914851866","doi":"https://doi.org/10.1109/lgrs.2018.2868862","title":"A Spatial-Spectral Disagreement-Based Sample Selection With an Application to Hyperspectral Data Classification","display_name":"A Spatial-Spectral Disagreement-Based Sample Selection With an Application to Hyperspectral Data Classification","publication_year":2019,"publication_date":"2019-02-05","ids":{"openalex":"https://openalex.org/W2914851866","doi":"https://doi.org/10.1109/lgrs.2018.2868862","mag":"2914851866"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2018.2868862","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2018.2868862","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":["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/A5037375081","display_name":"Micha\u0142 Cholewa","orcid":"https://orcid.org/0000-0001-6549-1590"},"institutions":[{"id":"https://openalex.org/I4210122744","display_name":"Institute of Theoretical and Applied Informatics","ror":"https://ror.org/037p52j25","country_code":"PL","type":"facility","lineage":["https://openalex.org/I4210122744","https://openalex.org/I99542240"]},{"id":"https://openalex.org/I99542240","display_name":"Polish Academy of Sciences","ror":"https://ror.org/01dr6c206","country_code":"PL","type":"government","lineage":["https://openalex.org/I99542240"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Michal Cholewa","raw_affiliation_strings":["Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland"],"raw_orcid":"https://orcid.org/0000-0001-6549-1590","affiliations":[{"raw_affiliation_string":"Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland","institution_ids":["https://openalex.org/I4210122744","https://openalex.org/I99542240"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061607408","display_name":"Przemys\u0142aw G\u0142omb","orcid":"https://orcid.org/0000-0002-0215-4674"},"institutions":[{"id":"https://openalex.org/I4210122744","display_name":"Institute of Theoretical and Applied Informatics","ror":"https://ror.org/037p52j25","country_code":"PL","type":"facility","lineage":["https://openalex.org/I4210122744","https://openalex.org/I99542240"]},{"id":"https://openalex.org/I99542240","display_name":"Polish Academy of Sciences","ror":"https://ror.org/01dr6c206","country_code":"PL","type":"government","lineage":["https://openalex.org/I99542240"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Przemyslaw Glomb","raw_affiliation_strings":["Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland","institution_ids":["https://openalex.org/I4210122744","https://openalex.org/I99542240"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5068582854","display_name":"Micha\u0142 Romaszewski","orcid":"https://orcid.org/0000-0002-8227-929X"},"institutions":[{"id":"https://openalex.org/I4210122744","display_name":"Institute of Theoretical and Applied Informatics","ror":"https://ror.org/037p52j25","country_code":"PL","type":"facility","lineage":["https://openalex.org/I4210122744","https://openalex.org/I99542240"]},{"id":"https://openalex.org/I99542240","display_name":"Polish Academy of Sciences","ror":"https://ror.org/01dr6c206","country_code":"PL","type":"government","lineage":["https://openalex.org/I99542240"]}],"countries":["PL"],"is_corresponding":false,"raw_author_name":"Michal Romaszewski","raw_affiliation_strings":["Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Theoretical and Applied Informatics, Polish Academy of Sciences, Gliwice, Poland","institution_ids":["https://openalex.org/I4210122744","https://openalex.org/I99542240"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.2686,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.82441491,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":"16","issue":"3","first_page":"467","last_page":"471"},"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/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9869999885559082,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9775999784469604,"subfield":{"id":"https://openalex.org/subfields/1602","display_name":"Analytical Chemistry"},"field":{"id":"https://openalex.org/fields/16","display_name":"Chemistry"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8310621976852417},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6687518358230591},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6626120805740356},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6293778419494629},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.5313463807106018},{"id":"https://openalex.org/keywords/markov-random-field","display_name":"Markov random field","score":0.4867403507232666},{"id":"https://openalex.org/keywords/sample","display_name":"Sample (material)","score":0.46612218022346497},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.44884711503982544},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4483446478843689},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.42842093110084534},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4193258583545685},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4192306399345398},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4167175889015198},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.40610364079475403},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2987110912799835},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.18467214703559875},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.12722375988960266},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.09329777956008911}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8310621976852417},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6687518358230591},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6626120805740356},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6293778419494629},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.5313463807106018},{"id":"https://openalex.org/C2778045648","wikidata":"https://www.wikidata.org/wiki/Q176827","display_name":"Markov random field","level":4,"score":0.4867403507232666},{"id":"https://openalex.org/C198531522","wikidata":"https://www.wikidata.org/wiki/Q485146","display_name":"Sample (material)","level":2,"score":0.46612218022346497},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.44884711503982544},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4483446478843689},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.42842093110084534},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4193258583545685},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4192306399345398},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4167175889015198},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.40610364079475403},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2987110912799835},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.18467214703559875},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.12722375988960266},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.09329777956008911},{"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/lgrs.2018.2868862","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2018.2868862","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"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.6000000238418579,"display_name":"Quality Education"}],"awards":[{"id":"https://openalex.org/G2209278278","display_name":null,"funder_award_id":"DEC-2011/03/D/ST6/03753","funder_id":"https://openalex.org/F4320322511","funder_display_name":"Narodowe Centrum Nauki"}],"funders":[{"id":"https://openalex.org/F4320322511","display_name":"Narodowe Centrum Nauki","ror":"https://ror.org/03ha2q922"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1479807131","https://openalex.org/W1482930835","https://openalex.org/W1536204915","https://openalex.org/W1591789348","https://openalex.org/W1824737917","https://openalex.org/W1998902551","https://openalex.org/W2030476695","https://openalex.org/W2039609561","https://openalex.org/W2048679005","https://openalex.org/W2049488676","https://openalex.org/W2087263574","https://openalex.org/W2087874437","https://openalex.org/W2092745549","https://openalex.org/W2094817543","https://openalex.org/W2126250169","https://openalex.org/W2129652905","https://openalex.org/W2131697388","https://openalex.org/W2136251662","https://openalex.org/W2143516773","https://openalex.org/W2148791530","https://openalex.org/W2153409933","https://openalex.org/W2154772499","https://openalex.org/W2158400785","https://openalex.org/W2160662337","https://openalex.org/W2171671120","https://openalex.org/W2478493250","https://openalex.org/W2485576679","https://openalex.org/W2528211483","https://openalex.org/W6679066655"],"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/W2027399350","https://openalex.org/W2044184146","https://openalex.org/W4313014865","https://openalex.org/W2147064750"],"abstract_inverted_index":{"A":[0],"significant":[1],"challenge":[2],"in":[3],"the":[4,9,27,51,54,61,75,78,83,98,107,115,134,146],"hyperspectral":[5],"data":[6,28],"classification":[7,116,125,147],"is":[8,121],"limited":[10],"number":[11],"of":[12,26,63,106,111,133],"available":[13],"training":[14,64,135],"samples.":[15],"Spatial-spectral":[16],"methods":[17],"approach":[18],"this":[19,41],"problem":[20],"by":[21,91],"employing":[22],"two":[23],"distinct":[24],"views":[25],"(spatial":[29],"and":[30,32,37,56,82,88,127,130,144],"spectral)":[31],"assuming":[33],"local":[34],"pixel":[35],"similarity":[36],"label":[38,99],"continuity.":[39],"Following":[40],"idea,":[42],"we":[43],"propose":[44],"a":[45,92],"sample":[46],"selection":[47],"method":[48,120],"that":[49],"exploits":[50],"diversity":[52,109],"between":[53,77],"spectral":[55],"spatial":[57,93],"information":[58],"to":[59,96,139],"extend":[60],"set":[62],"points.":[65],"New":[66],"seeds,":[67],"denoted":[68],"as":[69],"\u201cborderline":[70],"candidates,\u201d":[71],"are":[72,89],"derived":[73],"from":[74],"disagreement":[76],"support":[79],"vector":[80],"machine":[81],"Markov":[84],"random":[85],"field":[86],"classifiers":[87],"verified":[90],"neighborhood":[94],"voting":[95],"reduce":[97],"noise.":[100],"We":[101],"show":[102],"how":[103],"taking":[104],"advantage":[105],"learners'":[108],"(instead":[110],"their":[112],"consensus)":[113],"improves":[114],"result.":[117],"The":[118],"proposed":[119],"tested":[122],"with":[123],"several":[124],"algorithms":[126],"provides":[128],"reliable":[129],"useful":[131],"extension":[132],"set,":[136],"allowing":[137],"them":[138],"find":[140],"better":[141],"class":[142],"models":[143],"improve":[145],"accuracy.":[148]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":2}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}