{"id":"https://openalex.org/W2960273423","doi":"https://doi.org/10.1109/lgrs.2019.2923540","title":"Rough-Wavelet Feature Space, Deep Autoencoder, and Hyperspectral Image Classification","display_name":"Rough-Wavelet Feature Space, Deep Autoencoder, and Hyperspectral Image Classification","publication_year":2019,"publication_date":"2019-07-10","ids":{"openalex":"https://openalex.org/W2960273423","doi":"https://doi.org/10.1109/lgrs.2019.2923540","mag":"2960273423"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2019.2923540","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2019.2923540","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/A5073353175","display_name":"Saroj K. Meher","orcid":"https://orcid.org/0000-0002-1163-4768"},"institutions":[{"id":"https://openalex.org/I6498739","display_name":"Indian Statistical Institute","ror":"https://ror.org/00q2w1j53","country_code":"IN","type":"education","lineage":["https://openalex.org/I6498739"]}],"countries":["IN"],"is_corresponding":true,"raw_author_name":"Saroj K. Meher","raw_affiliation_strings":["Systems Science and Informatics Unit (SSIU), Indian Statistical Institute, Bangalore Centre, Bengaluru, India"],"affiliations":[{"raw_affiliation_string":"Systems Science and Informatics Unit (SSIU), Indian Statistical Institute, Bangalore Centre, Bengaluru, India","institution_ids":["https://openalex.org/I6498739"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5073353175"],"corresponding_institution_ids":["https://openalex.org/I6498739"],"apc_list":null,"apc_paid":null,"fwci":0.3571,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.64512033,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":"17","issue":"3","first_page":"489","last_page":"493"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9988999962806702,"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":0.9988999962806702,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9907000064849854,"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.9873999953269958,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.7753539085388184},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7746068835258484},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.7343305349349976},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.729829728603363},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7027374505996704},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6535126566886902},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.6266216039657593},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.5316342115402222},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5010280609130859},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4463401138782501},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.41798868775367737},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.30177003145217896},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.28573542833328247}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.7753539085388184},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7746068835258484},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.7343305349349976},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.729829728603363},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7027374505996704},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6535126566886902},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.6266216039657593},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.5316342115402222},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5010280609130859},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4463401138782501},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.41798868775367737},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.30177003145217896},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28573542833328247},{"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/lgrs.2019.2923540","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2019.2923540","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":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W2008847349","https://openalex.org/W2029316659","https://openalex.org/W2041100636","https://openalex.org/W2076063813","https://openalex.org/W2083541351","https://openalex.org/W2103094532","https://openalex.org/W2113236250","https://openalex.org/W2151288205","https://openalex.org/W2156005575","https://openalex.org/W2158633287","https://openalex.org/W2293771911","https://openalex.org/W2345128667","https://openalex.org/W2768309288","https://openalex.org/W4255833381","https://openalex.org/W6697079267"],"related_works":["https://openalex.org/W3013693939","https://openalex.org/W2159052453","https://openalex.org/W2566616303","https://openalex.org/W3131327266","https://openalex.org/W2072166414","https://openalex.org/W4297051394","https://openalex.org/W2752972570","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W2145836866"],"abstract_inverted_index":{"Prime":[0],"objective":[1],"of":[2,20,31,48,61,94,149,165,176],"this":[3],"letter":[4],"is":[5,42],"to":[6,108,112,133,170],"select":[7],"the":[8,13,29,67,80,84,101,113,131,147,163,166],"most":[9],"relevant":[10],"features":[11,81,99,105,137],"from":[12],"original":[14,102],"set":[15],"and":[16,72,87,100,119],"perform":[17],"two":[18,59,159],"steps":[19,60,129],"feature":[21,45,117],"extraction":[22,62,118],"operations":[23,63],"on":[24],"those":[25],"selected":[26,57],"set,":[27],"for":[28,44,116,146],"classification":[30,120,124,148],"hyperspectral":[32],"remote":[33],"sensing":[34],"(HSRS)":[35],"images.":[36,155],"Neighborhood":[37],"rough":[38],"sets":[39],"(NRSs)-based":[40],"method":[41],"used":[43],"selection":[46],"because":[47],"its":[49],"excellent":[50],"neighboring":[51],"information":[52,86,141],"capturing":[53],"ability.":[54],"On":[55],"these":[56,127],"features,":[58],"are":[64,106,143],"performed":[65],"using":[66],"stationary":[68],"wavelet":[69,98],"transform":[70],"(WT)":[71],"stacked":[73,88],"deep":[74],"autoencoder":[75],"(SDAE).":[76],"Stationary":[77],"WT":[78],"extracts":[79,90],"by":[82],"exploiting":[83],"spectral-spatial":[85,140],"DAE":[89,115],"through":[91],"representative":[92],"learning":[93],"input":[95,103,111],"information.":[96],"The":[97,122],"spectral":[104],"cascaded":[107],"feed":[109],"as":[110],"stacks":[114],"tasks.":[121],"proposed":[123,167],"model":[125,168],"with":[126,138,158],"operational":[128],"possesses":[130],"ability":[132],"capture":[134],"more":[135],"informative":[136],"improved":[139],"that":[142],"highly":[144],"beneficial":[145],"complex":[150],"data":[151],"sets,":[152],"like":[153],"HSRS":[154,160],"Simulation":[156],"results":[157],"images":[161],"justified":[162],"efficacy":[164],"compared":[169],"other":[171],"similar":[172],"methods":[173],"in":[174],"terms":[175],"different":[177],"performance":[178],"measurement":[179],"indexes.":[180]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}