{"id":"https://openalex.org/W2072518892","doi":"https://doi.org/10.1109/tgrs.2012.2185053","title":"Information Fusion in the Redundant-Wavelet-Transform Domain for Noise-Robust Hyperspectral Classification","display_name":"Information Fusion in the Redundant-Wavelet-Transform Domain for Noise-Robust Hyperspectral Classification","publication_year":2012,"publication_date":"2012-03-06","ids":{"openalex":"https://openalex.org/W2072518892","doi":"https://doi.org/10.1109/tgrs.2012.2185053","mag":"2072518892"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2012.2185053","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2012.2185053","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","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/A5037823063","display_name":"Saurabh Prasad","orcid":"https://orcid.org/0000-0003-3729-9360"},"institutions":[{"id":"https://openalex.org/I44461941","display_name":"University of Houston","ror":"https://ror.org/048sx0r50","country_code":"US","type":"education","lineage":["https://openalex.org/I44461941"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Saurabh Prasad","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA","Dept. of Electr. & Comput. Eng., Univ. Of Houston, Houston, TX, USA"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Houston, Houston, TX, USA","institution_ids":["https://openalex.org/I44461941"]},{"raw_affiliation_string":"Dept. of Electr. & Comput. Eng., Univ. Of Houston, Houston, TX, USA","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100318161","display_name":"Wei Li","orcid":"https://orcid.org/0000-0002-3135-0447"},"institutions":[{"id":"https://openalex.org/I99041443","display_name":"Mississippi State University","ror":"https://ror.org/0432jq872","country_code":"US","type":"education","lineage":["https://openalex.org/I4210141039","https://openalex.org/I99041443"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wei Li","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Mississippi State University, MS, USA","Dept. of Electr. & Comput. Eng., Mississippi State Univ., Starkville, MS, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Mississippi State University, MS, USA","institution_ids":["https://openalex.org/I99041443"]},{"raw_affiliation_string":"Dept. of Electr. & Comput. Eng., Mississippi State Univ., Starkville, MS, USA#TAB#","institution_ids":["https://openalex.org/I99041443"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078795882","display_name":"James E. Fowler","orcid":"https://orcid.org/0000-0003-2005-405X"},"institutions":[{"id":"https://openalex.org/I99041443","display_name":"Mississippi State University","ror":"https://ror.org/0432jq872","country_code":"US","type":"education","lineage":["https://openalex.org/I4210141039","https://openalex.org/I99041443"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"James E. Fowler","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Mississippi State University, MS, USA","Dept. of Electr. & Comput. Eng., Mississippi State Univ., Starkville, MS, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Mississippi State University, MS, USA","institution_ids":["https://openalex.org/I99041443"]},{"raw_affiliation_string":"Dept. of Electr. & Comput. Eng., Mississippi State Univ., Starkville, MS, USA#TAB#","institution_ids":["https://openalex.org/I99041443"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5103103246","display_name":"L.M. Bruce","orcid":"https://orcid.org/0000-0002-9380-1143"},"institutions":[{"id":"https://openalex.org/I99041443","display_name":"Mississippi State University","ror":"https://ror.org/0432jq872","country_code":"US","type":"education","lineage":["https://openalex.org/I4210141039","https://openalex.org/I99041443"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lori M. Bruce","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Mississippi State University, MS, USA","Dept. of Electr. & Comput. Eng., Mississippi State Univ., Starkville, MS, USA#TAB#"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Mississippi State University, MS, USA","institution_ids":["https://openalex.org/I99041443"]},{"raw_affiliation_string":"Dept. of Electr. & Comput. Eng., Mississippi State Univ., Starkville, MS, USA#TAB#","institution_ids":["https://openalex.org/I99041443"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5037823063"],"corresponding_institution_ids":["https://openalex.org/I44461941"],"apc_list":null,"apc_paid":null,"fwci":9.3693,"has_fulltext":false,"cited_by_count":55,"citation_normalized_percentile":{"value":0.97943401,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"50","issue":"9","first_page":"3474","last_page":"3486"},"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9962999820709229,"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/T10640","display_name":"Spectroscopy and Chemometric Analyses","score":0.9950000047683716,"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":0.8241356611251831},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7402269840240479},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7010596990585327},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6179393529891968},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.5518308281898499},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5381404161453247},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.5145963430404663},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"Noise reduction","score":0.5117566585540771},{"id":"https://openalex.org/keywords/wavelet","display_name":"Wavelet","score":0.47640883922576904},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.4687141478061676},{"id":"https://openalex.org/keywords/wavelet-transform","display_name":"Wavelet transform","score":0.4685547947883606},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4271673262119293},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.4243719279766083},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.08539959788322449}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8241356611251831},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7402269840240479},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7010596990585327},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6179393529891968},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5518308281898499},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5381404161453247},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.5145963430404663},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.5117566585540771},{"id":"https://openalex.org/C47432892","wikidata":"https://www.wikidata.org/wiki/Q831390","display_name":"Wavelet","level":2,"score":0.47640883922576904},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.4687141478061676},{"id":"https://openalex.org/C196216189","wikidata":"https://www.wikidata.org/wiki/Q2867","display_name":"Wavelet transform","level":3,"score":0.4685547947883606},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4271673262119293},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.4243719279766083},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.08539959788322449},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tgrs.2012.2185053","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2012.2185053","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.434.6749","is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.434.6749","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.ece.msstate.edu/~fowler/Publications/Papers/PLF2012.pdf","raw_type":"text"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6600000262260437,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W1512748702","https://openalex.org/W1541836152","https://openalex.org/W1570876803","https://openalex.org/W1985996976","https://openalex.org/W2046472904","https://openalex.org/W2047016135","https://openalex.org/W2096201283","https://openalex.org/W2108009806","https://openalex.org/W2117146861","https://openalex.org/W2120348721","https://openalex.org/W2121561691","https://openalex.org/W2132532785","https://openalex.org/W2135346934","https://openalex.org/W2148198942","https://openalex.org/W2151599207","https://openalex.org/W2153820018","https://openalex.org/W2154874087","https://openalex.org/W2156781641","https://openalex.org/W2161073299","https://openalex.org/W2161717801","https://openalex.org/W2166650389","https://openalex.org/W2168175751","https://openalex.org/W3168504385","https://openalex.org/W4205687621","https://openalex.org/W4251564091","https://openalex.org/W4256068510","https://openalex.org/W6796689616"],"related_works":["https://openalex.org/W2132083814","https://openalex.org/W2292979300","https://openalex.org/W1995622179","https://openalex.org/W1484111231","https://openalex.org/W1552543208","https://openalex.org/W2074396517","https://openalex.org/W2166963679","https://openalex.org/W2187269125","https://openalex.org/W1641615907","https://openalex.org/W3089231081"],"abstract_inverted_index":{"Hyperspectral":[0],"imagery":[1],"comprises":[2],"high-dimensional":[3,23,118],"reflectance":[4],"vectors":[5],"representing":[6,92],"the":[7,19,70,83,93,156,179],"spectral":[8,142],"response":[9],"over":[10],"a":[11,63,87,96,107,131,140,145,153,160],"wide":[12],"range":[13],"of":[14,73,126,133,155],"wavelengths":[15],"per":[16],"pixel":[17],"in":[18,28,95,117],"image.":[20],"The":[21,123],"resulting":[22],"feature":[24,97,128,157],"spaces":[25,119],"often":[26],"result":[27],"statistically":[29],"ill-conditioned":[30],"class-conditional":[31],"distributions.":[32],"Conventional":[33],"methods":[34,54],"for":[35,76,91,112,159,167],"alleviating":[36],"this":[37,127],"problem":[38],"typically":[39],"employ":[40],"dimensionality":[41,72],"reduction":[42],"such":[43,152],"as":[44,104,106],"linear":[45],"discriminant":[46],"analysis":[47,103],"along":[48],"with":[49,173],"single-classifier":[50],"systems,":[51],"yet":[52],"these":[53],"are":[55],"suboptimal":[56],"and":[57,78,109,114,186],"lack":[58],"noise":[59,165],"robustness.":[60],"In":[61],"contrast,":[62],"divide-and-conquer":[64],"approach":[65],"is":[66,86,149],"proposed":[67,84,124,180],"to":[68,82,100,144],"address":[69],"high":[71],"hyperspectral":[74,175],"data":[75,94,176],"effective":[77],"noise-robust":[79,101],"classification.":[80],"Central":[81],"framework":[85],"redundant":[88],"wavelet":[89],"transform":[90],"space":[98,129,158],"amenable":[99],"multiscale":[102],"well":[105],"multiclassifier":[108,161],"decision-fusion":[110],"system":[111,162,181],"classification":[113,168,187],"target":[115],"recognition":[116],"under":[120],"small-sample-size":[121],"conditions.":[122],"partitioning":[125,154],"assigns":[130],"collection":[132],"all":[134,137],"coefficients":[135],"across":[136],"scales":[138],"at":[139],"particular":[141],"wavelength":[143],"dedicated":[146],"classifier.":[147],"It":[148],"demonstrated":[150],"that":[151,178],"yields":[163],"superior":[164],"performance":[166],"tasks.":[169],"Additionally,":[170],"validation":[171],"studies":[172],"experimental":[174],"show":[177],"significantly":[182],"outperforms":[183],"conventional":[184],"denoising":[185],"approaches.":[188]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":6},{"year":2020,"cited_by_count":7},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":6},{"year":2016,"cited_by_count":9},{"year":2015,"cited_by_count":5},{"year":2014,"cited_by_count":9},{"year":2013,"cited_by_count":3},{"year":2012,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}