{"id":"https://openalex.org/W2954958556","doi":"https://doi.org/10.1109/whispers.2018.8747090","title":"Hyperspectral Image Classification Based on Extended Morphological Attribute Profiles and Abundance Information","display_name":"Hyperspectral Image Classification Based on Extended Morphological Attribute Profiles and Abundance Information","publication_year":2018,"publication_date":"2018-09-01","ids":{"openalex":"https://openalex.org/W2954958556","doi":"https://doi.org/10.1109/whispers.2018.8747090","mag":"2954958556"},"language":"en","primary_location":{"id":"doi:10.1109/whispers.2018.8747090","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2018.8747090","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/A5101588866","display_name":"Xia Zhang","orcid":"https://orcid.org/0000-0002-4313-600X"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210128053","display_name":"Institute of Remote Sensing and Digital Earth","ror":"https://ror.org/02cjszf03","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210128053"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xia Zhang","raw_affiliation_strings":["Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences, Institute of Remote Sensing and Digital Earth, Beijing, China","institution_ids":["https://openalex.org/I4210128053","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5061841145","display_name":"Yanli Sun","orcid":"https://orcid.org/0000-0003-3144-5558"},"institutions":[{"id":"https://openalex.org/I4210130112","display_name":"China Academy of Information and Communications Technology","ror":"https://ror.org/038dte259","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210130112","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanli sun","raw_affiliation_strings":["China Academy of Electronics and Information Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Academy of Electronics and Information Technology","institution_ids":["https://openalex.org/I4210130112"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5018037147","display_name":"Wenchao Qi","orcid":"https://orcid.org/0000-0003-3465-5278"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wenchao Qi","raw_affiliation_strings":["University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.6275,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.76754635,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"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":0.9998000264167786,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9944000244140625,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.947700023651123,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.9384138584136963},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7483230233192444},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6634656190872192},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6144812107086182},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.5490943193435669},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5415624976158142},{"id":"https://openalex.org/keywords/multinomial-logistic-regression","display_name":"Multinomial logistic regression","score":0.5007753372192383},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4987971782684326},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.4829525351524353},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.46958819031715393},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.45222359895706177},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.39723992347717285},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2871909737586975},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.16767814755439758},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.13816285133361816}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9384138584136963},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7483230233192444},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6634656190872192},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6144812107086182},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5490943193435669},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5415624976158142},{"id":"https://openalex.org/C117568660","wikidata":"https://www.wikidata.org/wiki/Q1650843","display_name":"Multinomial logistic regression","level":2,"score":0.5007753372192383},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4987971782684326},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.4829525351524353},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.46958819031715393},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.45222359895706177},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.39723992347717285},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2871909737586975},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.16767814755439758},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.13816285133361816}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/whispers.2018.8747090","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2018.8747090","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":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W2005672614","https://openalex.org/W2041227601","https://openalex.org/W2083541351","https://openalex.org/W2084252873","https://openalex.org/W2114819256","https://openalex.org/W2127199143","https://openalex.org/W2157321686","https://openalex.org/W2159070926","https://openalex.org/W2603834682","https://openalex.org/W2613575128","https://openalex.org/W2757242159","https://openalex.org/W2767805377","https://openalex.org/W2774723322"],"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/W2019190440","https://openalex.org/W2132083814"],"abstract_inverted_index":{"Due":[0],"to":[1,53,66,85],"the":[2,9,15,38,54,68,74,87,98,106,115,120,130],"high":[3],"dimensionality":[4],"of":[5,11,44,124],"hyperspectral":[6,23,32,45],"data":[7],"and":[8,41,49,73,79,110,137,142],"present":[10],"mixed":[12],"pixels":[13],"in":[14,22],"image,":[16],"classification":[17,34,116,125],"is":[18],"a":[19,29],"challenging":[20],"task":[21],"image":[24,33,46],"applications.":[25],"In":[26],"this":[27],"paper,":[28],"strategy":[30],"for":[31,90],"was":[35,64,83],"presented,":[36],"where":[37],"spectral-spatial":[39],"features":[40],"abundance":[42,88,111,138],"information":[43,89,112,139],"were":[47,95],"extracted":[48],"provided":[50],"as":[51],"input":[52],"sparse":[55,80],"multinomial":[56],"logistic":[57],"regression":[58],"classifier.":[59],"The":[60,93],"mathematical":[61],"morphology":[62],"method":[63,82],"used":[65,84],"extract":[67],"extended":[69,107,134],"multi-attribute":[70,108,135],"profiles":[71,109,136],"(EMAPs),":[72],"class-based":[75],"end":[76],"member":[77],"extraction":[78],"unmixing":[81],"obtain":[86],"each":[91],"class.":[92],"experiments":[94],"conducted":[96],"on":[97],"well-known":[99],"Indian":[100],"Pines":[101],"dataset.":[102],"Results":[103],"indicated":[104],"that":[105],"significantly":[113],"improved":[114],"performance.":[117],"Compared":[118],"with":[119],"overall":[121,131],"accuracy":[122,132],"80.46%":[123],"using":[126,133],"original":[127],"spectral":[128],"bands,":[129],"reached":[140],"94.27%":[141],"88.75%,":[143],"respectively.":[144]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}