{"id":"https://openalex.org/W2780414011","doi":"https://doi.org/10.1109/dicta.2017.8227490","title":"Spectral-Spatial Hyperspectral Image Classification via Boundary-Adaptive Deep Learning","display_name":"Spectral-Spatial Hyperspectral Image Classification via Boundary-Adaptive Deep Learning","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2780414011","doi":"https://doi.org/10.1109/dicta.2017.8227490","mag":"2780414011"},"language":"en","primary_location":{"id":"doi:10.1109/dicta.2017.8227490","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta.2017.8227490","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","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/A5091685573","display_name":"Atif Mughees","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Atif Mughees","raw_affiliation_strings":["Pervasive Computing Division, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Pervasive Computing Division, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101014809","display_name":"Linmi Tao","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linmi Tao","raw_affiliation_strings":["Pervasive Computing Division, Tsinghua University, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Pervasive Computing Division, Tsinghua University, Beijing, China","institution_ids":["https://openalex.org/I99065089"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5091685573"],"corresponding_institution_ids":["https://openalex.org/I99065089"],"apc_list":null,"apc_paid":null,"fwci":0.4708,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.72524598,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":96},"biblio":{"volume":"8","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9932000041007996,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9890999794006348,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7736438512802124},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7338048219680786},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7144673466682434},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6894413828849792},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5766586065292358},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5514324307441711},{"id":"https://openalex.org/keywords/spatial-analysis","display_name":"Spatial analysis","score":0.5443037152290344},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5066841244697571},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4616386592388153},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4422208070755005},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2520913779735565},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.18908187747001648},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.08163875341415405}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7736438512802124},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7338048219680786},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7144673466682434},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6894413828849792},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5766586065292358},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5514324307441711},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.5443037152290344},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5066841244697571},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4616386592388153},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4422208070755005},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2520913779735565},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.18908187747001648},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.08163875341415405},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/dicta.2017.8227490","is_oa":false,"landing_page_url":"https://doi.org/10.1109/dicta.2017.8227490","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 International Conference on Digital Image Computing: Techniques and Applications (DICTA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5199999809265137,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1521436688","https://openalex.org/W1616262590","https://openalex.org/W1984541135","https://openalex.org/W2001298023","https://openalex.org/W2016860790","https://openalex.org/W2019338222","https://openalex.org/W2029316659","https://openalex.org/W2041100636","https://openalex.org/W2078495963","https://openalex.org/W2090424610","https://openalex.org/W2257669061","https://openalex.org/W2412588858","https://openalex.org/W2547322152","https://openalex.org/W2557594442","https://openalex.org/W2793760436","https://openalex.org/W3101640299","https://openalex.org/W3102431071","https://openalex.org/W3135396862","https://openalex.org/W4240485910","https://openalex.org/W6749185463"],"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/W2404757046","https://openalex.org/W2044184146","https://openalex.org/W2795259429","https://openalex.org/W2565656575"],"abstract_inverted_index":{"Deep":[0],"learning":[1],"based":[2,115],"hyperspectral":[3],"image":[4],"(HSI)":[5],"classification":[6,112,152,168],"have":[7],"recently":[8],"shown":[9],"promising":[10],"performance.":[11,153],"However,":[12],"complex":[13],"network":[14,27,52,71],"architecture,":[15],"tedious":[16],"training":[17],"process":[18],"and":[19,31,62,102,123,140],"effective":[20,41,80],"utilization":[21],"of":[22,33,90,136],"spatial/contextual":[23],"information":[24,61,99],"in":[25],"deep":[26,34,48,70,145],"limits":[28],"the":[29,87,151],"application":[30],"performance":[32],"learning.":[35],"In":[36],"this":[37],"paper,":[38],"for":[39,79,130],"an":[40,46,110],"spectral-spatial":[42,132],"feature":[43,77],"extraction":[44],",":[45],"improved":[47],"network,":[49],"spatial":[50,59,97],"adaptive":[51],"(SANet)":[53],"approach":[54],"is":[55,84,100],"proposed":[56,163],"which":[57,72],"exploits":[58],"contextual":[60],"spectral":[63,106,124,141],"characteristics":[64,125],"to":[65,74],"construct":[66],"a":[67,91,127],"more":[68,75],"simplified":[69,144],"leads":[73],"powerful":[76],"representation":[78],"HSI":[81,133,159],"classification.":[82,134],"SANet":[83,114,128,146,164],"established":[85],"from":[86],"simple":[88],"structure":[89],"principal":[92],"component":[93],"analysis":[94],"network.":[95],"First":[96],"structural":[98,138],"extracted":[101],"combined":[103],"with":[104],"informative":[105],"channels":[107],"followed":[108],"by":[109],"object-level":[111],"using":[113],"decision":[116],"fusion":[117],"approach.":[118],"It":[119],"integrates":[120],"spatial-contextual":[121],"outcome":[122],"into":[126,143],"framework":[129],"robust":[131],"Integration":[135],"local":[137],"regularity":[139],"similarity":[142],"has":[147],"significant":[148],"effect":[149],"on":[150,156],"Experimental":[154],"results":[155,169],"popular":[157],"standard":[158],"datasets":[160],"reveal":[161],"that":[162],"technique":[165],"produce":[166],"better":[167],"than":[170],"existing":[171],"well":[172],"known":[173],"techniques.":[174]},"counts_by_year":[{"year":2018,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}