{"id":"https://openalex.org/W2965262829","doi":"https://doi.org/10.1080/09540091.2019.1650330","title":"CNN-based salient features in HSI image semantic target prediction","display_name":"CNN-based salient features in HSI image semantic target prediction","publication_year":2019,"publication_date":"2019-08-05","ids":{"openalex":"https://openalex.org/W2965262829","doi":"https://doi.org/10.1080/09540091.2019.1650330","mag":"2965262829"},"language":"en","primary_location":{"id":"doi:10.1080/09540091.2019.1650330","is_oa":true,"landing_page_url":"https://doi.org/10.1080/09540091.2019.1650330","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/09540091.2019.1650330?needAccess=true","source":{"id":"https://openalex.org/S4210188800","display_name":"Connection Science","issn_l":"0954-0091","issn":["0954-0091","1360-0494"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Connection Science","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://www.tandfonline.com/doi/pdf/10.1080/09540091.2019.1650330?needAccess=true","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5090862543","display_name":"Vishal Srivastava","orcid":"https://orcid.org/0000-0002-2064-5805"},"institutions":[{"id":"https://openalex.org/I56404289","display_name":"Indian Institute of Technology BHU","ror":"https://ror.org/01kh5gc44","country_code":"IN","type":"education","lineage":["https://openalex.org/I56404289"]},{"id":"https://openalex.org/I91357014","display_name":"Banaras Hindu University","ror":"https://ror.org/04cdn2797","country_code":"IN","type":"education","lineage":["https://openalex.org/I91357014"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Vishal Srivastava","raw_affiliation_strings":["Department of Computer Engineering, IIT (BHU), Varanasi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, IIT (BHU), Varanasi, India","institution_ids":["https://openalex.org/I56404289","https://openalex.org/I91357014"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084923728","display_name":"Bhaskar Biswas","orcid":"https://orcid.org/0000-0001-9762-3834"},"institutions":[{"id":"https://openalex.org/I56404289","display_name":"Indian Institute of Technology BHU","ror":"https://ror.org/01kh5gc44","country_code":"IN","type":"education","lineage":["https://openalex.org/I56404289"]},{"id":"https://openalex.org/I91357014","display_name":"Banaras Hindu University","ror":"https://ror.org/04cdn2797","country_code":"IN","type":"education","lineage":["https://openalex.org/I91357014"]}],"countries":["IN"],"is_corresponding":false,"raw_author_name":"Bhaskar Biswas","raw_affiliation_strings":["Department of Computer Engineering, IIT (BHU), Varanasi, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer Engineering, IIT (BHU), Varanasi, India","institution_ids":["https://openalex.org/I56404289","https://openalex.org/I91357014"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5090862543"],"corresponding_institution_ids":["https://openalex.org/I56404289","https://openalex.org/I91357014"],"apc_list":{"value":1270,"currency":"USD","value_usd":1270},"apc_paid":null,"fwci":4.3493,"has_fulltext":false,"cited_by_count":29,"citation_normalized_percentile":{"value":0.94875,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":94,"max":99},"biblio":{"volume":"32","issue":"2","first_page":"113","last_page":"131"},"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9980999827384949,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9979000091552734,"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/computer-science","display_name":"Computer science","score":0.8038687705993652},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7814485430717468},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7576488852500916},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.543535590171814},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5194434523582458},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5080220699310303},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.5045484304428101},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.4738198518753052},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.44548314809799194},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.4380127191543579}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8038687705993652},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7814485430717468},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7576488852500916},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.543535590171814},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5194434523582458},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5080220699310303},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.5045484304428101},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.4738198518753052},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.44548314809799194},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.4380127191543579},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"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":2,"locations":[{"id":"doi:10.1080/09540091.2019.1650330","is_oa":true,"landing_page_url":"https://doi.org/10.1080/09540091.2019.1650330","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/09540091.2019.1650330?needAccess=true","source":{"id":"https://openalex.org/S4210188800","display_name":"Connection Science","issn_l":"0954-0091","issn":["0954-0091","1360-0494"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Connection Science","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:beaef934d5b940b3b6419831824ebe8f","is_oa":false,"landing_page_url":"https://doaj.org/article/beaef934d5b940b3b6419831824ebe8f","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Connection Science, Vol 32, Iss 2, Pp 113-131 (2020)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1080/09540091.2019.1650330","is_oa":true,"landing_page_url":"https://doi.org/10.1080/09540091.2019.1650330","pdf_url":"https://www.tandfonline.com/doi/pdf/10.1080/09540091.2019.1650330?needAccess=true","source":{"id":"https://openalex.org/S4210188800","display_name":"Connection Science","issn_l":"0954-0091","issn":["0954-0091","1360-0494"],"is_oa":false,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Connection Science","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W146900863","https://openalex.org/W1521436688","https://openalex.org/W1699734612","https://openalex.org/W1972524915","https://openalex.org/W1994002998","https://openalex.org/W2054813538","https://openalex.org/W2080142539","https://openalex.org/W2097117768","https://openalex.org/W2151103935","https://openalex.org/W2152057649","https://openalex.org/W2169500530","https://openalex.org/W2294492906","https://openalex.org/W2314785379","https://openalex.org/W2342491128","https://openalex.org/W2358876993","https://openalex.org/W2502206489","https://openalex.org/W2512351403","https://openalex.org/W2548791488","https://openalex.org/W2592962403","https://openalex.org/W2605793178","https://openalex.org/W2618530766","https://openalex.org/W2732412926","https://openalex.org/W2759518055","https://openalex.org/W2768537477","https://openalex.org/W2773771410","https://openalex.org/W2779530678","https://openalex.org/W2783165089","https://openalex.org/W2792625921","https://openalex.org/W2809635958","https://openalex.org/W2886855110","https://openalex.org/W2887361581","https://openalex.org/W2913290113","https://openalex.org/W2923349434","https://openalex.org/W2941590339","https://openalex.org/W2944195984","https://openalex.org/W3103856189"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2070598848","https://openalex.org/W3034375524","https://openalex.org/W2060875994","https://openalex.org/W2027399350","https://openalex.org/W2044184146","https://openalex.org/W2019190440","https://openalex.org/W2343470940","https://openalex.org/W3034864990"],"abstract_inverted_index":{"Deep":[0],"networks":[1],"have":[2,30,47,73,133,147,158],"escalated":[3],"the":[4,8,32,40,54,58,67,85,93,99,104,115,127,137,169],"computational":[5,100,164],"performance":[6],"in":[7,26,57],"sensor-based":[9],"high":[10,160],"dimensional":[11],"imaging":[12],"such":[13],"as":[14,166],"hyperspectral":[15],"images":[16],"(HSI),":[17],"due":[18],"to":[19,52,83,97,136,168],"their":[20],"informative":[21,33,55,87,95],"feature":[22,62,81,110],"extraction":[23],"competency.":[24],"Therefore":[25],"this":[27],"work,":[28],"we":[29,72,146],"extracted":[31],"features":[34,46,51,88,96,132,151],"from":[35],"different":[36],"CNN":[37],"models":[38],"for":[39,142],"benchmark":[41],"HSI":[42],"datasets.":[43],"The":[44,61],"deep":[45],"concatenated":[48],"with":[49,162],"spectral":[50],"increase":[53],"knowledge":[56],"image":[59],"datacube.":[60,70],"concatenation":[63],"has":[64],"massively":[65],"increased":[66],"size":[68],"of":[69,89],"Therefore,":[71],"applied":[74],"an":[75,108],"unsupervised":[76,109],"maximum":[77],"object":[78],"identification-based":[79],"salient":[80,150],"selection":[82,111],"identify":[84],"most":[86],"datacube":[90],"and":[91,120,123,157],"discard":[92],"less":[94],"reduce":[98],"time":[101,165],"without":[102],"compromising":[103],"accuracy.":[105],"It":[106],"is":[107],"approach":[112],"that":[113],"transforms":[114],"data":[116],"into":[117,152],"scale":[118],"space":[119],"achieved":[121,159],"robust":[122],"strong":[124],"features.":[125],"In":[126],"previous":[128,170],"CNN-based":[129],"methods,":[130],"raw":[131],"directly":[134],"fed":[135],"MLP":[138],"(multilayer":[139],"perception)":[140],"layers":[141],"target":[143],"prediction":[144],"whereas":[145],"provided":[148],"our":[149],"a":[153],"multi-core":[154],"SVM-based":[155],"set-up":[156],"accuracy":[161],"low":[163],"compared":[167],"state-of-art":[171],"techniques.":[172]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":5},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":10}],"updated_date":"2026-05-19T21:40:30.786675","created_date":"2025-10-10T00:00:00"}
