{"id":"https://openalex.org/W1997217899","doi":"https://doi.org/10.1109/acssc.2013.6810399","title":"Sparse representations for classification of high dimensional multi-sensor geospatial data","display_name":"Sparse representations for classification of high dimensional multi-sensor geospatial data","publication_year":2013,"publication_date":"2013-11-01","ids":{"openalex":"https://openalex.org/W1997217899","doi":"https://doi.org/10.1109/acssc.2013.6810399","mag":"1997217899"},"language":"en","primary_location":{"id":"doi:10.1109/acssc.2013.6810399","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acssc.2013.6810399","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 Asilomar Conference on Signals, Systems and Computers","raw_type":"proceedings-article"},"type":"conference-paper","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":false,"raw_author_name":"Saurabh Prasad","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Houston Houston, Texas","Department of Electrical and Computer Engineering University of Houston  Houston Texas"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Houston Houston, Texas","institution_ids":["https://openalex.org/I44461941"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering University of Houston  Houston Texas","institution_ids":["https://openalex.org/I44461941"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5111740765","display_name":"Minshan Cui","orcid":null},"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":false,"raw_author_name":"Minshan Cui","raw_affiliation_strings":["Department of Electrical and Computer Engineering, University of Houston Houston, Texas","Department of Electrical and Computer Engineering University of Houston  Houston Texas"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, University of Houston Houston, Texas","institution_ids":["https://openalex.org/I44461941"]},{"raw_affiliation_string":"Department of Electrical and Computer Engineering University of Houston  Houston Texas","institution_ids":["https://openalex.org/I44461941"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I44461941"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2206","display_name":"Computational Mechanics"},"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9991999864578247,"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/T12015","display_name":"Photoacoustic and Ultrasonic Imaging","score":0.9979000091552734,"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":0.7353920936584473},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7205036878585815},{"id":"https://openalex.org/keywords/sparse-approximation","display_name":"Sparse approximation","score":0.6697163581848145},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6512255668640137},{"id":"https://openalex.org/keywords/geospatial-analysis","display_name":"Geospatial analysis","score":0.5730506181716919},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5696319341659546},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5142931342124939},{"id":"https://openalex.org/keywords/lidar","display_name":"Lidar","score":0.46072036027908325},{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.44681692123413086},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.4403125047683716},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.31248003244400024}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7353920936584473},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7205036878585815},{"id":"https://openalex.org/C124066611","wikidata":"https://www.wikidata.org/wiki/Q28684319","display_name":"Sparse approximation","level":2,"score":0.6697163581848145},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6512255668640137},{"id":"https://openalex.org/C9770341","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Geospatial analysis","level":2,"score":0.5730506181716919},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5696319341659546},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5142931342124939},{"id":"https://openalex.org/C51399673","wikidata":"https://www.wikidata.org/wiki/Q504027","display_name":"Lidar","level":2,"score":0.46072036027908325},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.44681692123413086},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.4403125047683716},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.31248003244400024},{"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},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/acssc.2013.6810399","is_oa":false,"landing_page_url":"https://doi.org/10.1109/acssc.2013.6810399","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 Asilomar Conference on Signals, Systems and Computers","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":27,"referenced_works":["https://openalex.org/W162963782","https://openalex.org/W1970099214","https://openalex.org/W1976935203","https://openalex.org/W2036803867","https://openalex.org/W2038386419","https://openalex.org/W2067782748","https://openalex.org/W2069231830","https://openalex.org/W2070127246","https://openalex.org/W2073114111","https://openalex.org/W2078296814","https://openalex.org/W2109531142","https://openalex.org/W2121058967","https://openalex.org/W2126067108","https://openalex.org/W2127271355","https://openalex.org/W2129812935","https://openalex.org/W2132467081","https://openalex.org/W2138424157","https://openalex.org/W2138535296","https://openalex.org/W2163957348","https://openalex.org/W2295820431","https://openalex.org/W2545783767","https://openalex.org/W4233760599","https://openalex.org/W4285719527","https://openalex.org/W6606614799","https://openalex.org/W6659718526","https://openalex.org/W6680211732","https://openalex.org/W6728949182"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2070598848","https://openalex.org/W3034375524","https://openalex.org/W2027399350","https://openalex.org/W2132083814","https://openalex.org/W2374021060","https://openalex.org/W2258058088","https://openalex.org/W3125756894","https://openalex.org/W1826223126"],"abstract_inverted_index":{"Modern":[0],"active":[1],"and":[2,70,148,222,226],"passive":[3],"geospatial":[4,153,217],"sensing":[5],"modalities":[6],"often":[7,60,74],"result":[8],"in":[9,17,65,77,133],"high":[10,50,215],"dimensional":[11,68,216],"feature":[12,47],"spaces.":[13],"Hyper-spectral":[14],"imagery":[15],"results":[16],"per-pixel":[18],"spectral":[19,29],"\u201csignatures\u201d":[20],"that":[21,159,209],"represent":[22],"the":[23,36,40,45,52,56,107,113,118,141,160,176,212],"reflectance/radiance":[24],"over":[25],"hundreds":[26],"of":[27,39,55,109,120,143,178,197,214],"contiguous":[28],"bands.":[30],"Likewise,":[31],"full-waveform":[32,223],"LiDAR":[33,224],"systems":[34],"record":[35],"entire":[37,114],"waveform":[38],"return":[41],"Laser":[42],"pulse.":[43],"Although":[44,129],"resulting":[46],"spaces":[48],"are":[49],"dimensional,":[51],"underlying":[53],"dimensionality":[54,213],"information":[57,63],"content":[58],"is":[59,73,99,145,180],"small":[61],"(i.e.,":[62],"resides":[64],"a":[66,205],"lower":[67],"subspace),":[69],"such":[71],"data":[72,218],"sparsely":[75],"represented":[76],"an":[78,102,188],"appropriate":[79],"dictionary.":[80],"In":[81,183,200],"recent":[82,184],"work,":[83,185],"sparse":[84,193,236],"representation":[85,108,237],"based":[86,126],"classification":[87,196],"has":[88,131],"been":[89],"utilized":[90],"for":[91,136,152,170,195],"effective":[92],"face":[93,137],"recognition":[94,138],"tasks.":[95],"The":[96],"traditional":[97,171],"solution":[98],"posed":[100],"as":[101],"optimization":[103],"problem":[104],"to":[105,149,166,191],"learn":[106],"test":[110],"samples":[111],"using":[112],"training":[115],"dictionary":[116],"under":[117],"constraint":[119],"sparsity":[121],"-":[122],"i.e.,":[123],"Sparse":[124],"Representation":[125],"Classification":[127],"(SRC).":[128],"this":[130,201,233],"resulted":[132],"promising":[134],"performance":[135,169],"tasks":[139,174],"(where":[140],"number":[142,177],"classes":[144,179],"very":[146],"large),":[147],"some":[150],"extent":[151],"image":[154,172],"analysis":[155,173],"tasks,":[156],"we":[157,186,203],"contend":[158],"SRC":[161],"formulation":[162],"should":[163],"be":[164],"modified":[165],"yield":[167],"robust":[168],"where":[175],"not":[181],"large.":[182],"developed":[187,235],"alternate":[189],"approach":[190],"use":[192],"representations":[194],"hyperspectral":[198,220],"imagery.":[199],"paper,":[202],"develop":[204],"subspace":[206],"learning":[207],"preprocessing":[208],"effectively":[210],"reduces":[211],"(both":[219],"imagery,":[221],"data),":[225],"demonstrate":[227],"its":[228],"efficacy":[229],"when":[230],"combined":[231],"with":[232],"recently":[234],"approach.":[238]},"counts_by_year":[{"year":2021,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
