{"id":"https://openalex.org/W6945869588","doi":"https://doi.org/10.26190/unsworks/2565","title":"Subspace Detection Approaches for Hyperspectral Image Classification","display_name":"Subspace Detection Approaches for Hyperspectral Image Classification","publication_year":2014,"publication_date":"2014-01-01","ids":{"openalex":"https://openalex.org/W6945869588","doi":"https://doi.org/10.26190/unsworks/2565"},"language":"en","primary_location":{"id":"pmh:oai:unsworks.library.unsw.edu.au:1959.4/53507","is_oa":true,"landing_page_url":"http://hdl.handle.net/1959.4/53507","pdf_url":"https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12202/SOURCE02?view=true","source":{"id":"https://openalex.org/S4306401737","display_name":"UNSWorks (University of New South Wales, Sydney, Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I40053085","host_organization_name":"Australian Defence Force Academy","host_organization_lineage":["https://openalex.org/I40053085"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"http://purl.org/coar/resource_type/c_db06"},"type":"dissertation","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12202/SOURCE02?view=true","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Hossain, Md Ali","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Hossain, Md Ali","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"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":true,"primary_topic":{"id":"https://openalex.org/T12314","display_name":"Nuts composition and effects","score":0.4823000133037567,"subfield":{"id":"https://openalex.org/subfields/2916","display_name":"Nutrition and Dietetics"},"field":{"id":"https://openalex.org/fields/29","display_name":"Nursing"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T12314","display_name":"Nuts composition and effects","score":0.4823000133037567,"subfield":{"id":"https://openalex.org/subfields/2916","display_name":"Nutrition and Dietetics"},"field":{"id":"https://openalex.org/fields/29","display_name":"Nursing"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13168","display_name":"Plant Ecology and Taxonomy Studies","score":0.03680000081658363,"subfield":{"id":"https://openalex.org/subfields/1105","display_name":"Ecology, Evolution, Behavior and Systematics"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T13651","display_name":"Medicinal Plants and Bioactive Compounds","score":0.019600000232458115,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.7694000005722046},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.7310000061988831},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.6205000281333923},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.5914999842643738},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5587000250816345},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.5475000143051147},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.545799970626831},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.4941999912261963},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.44830000400543213},{"id":"https://openalex.org/keywords/kernel-principal-component-analysis","display_name":"Kernel principal component analysis","score":0.4350000023841858}],"concepts":[{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.7694000005722046},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.7310000061988831},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6714000105857849},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.6205000281333923},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.5914999842643738},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.579200029373169},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5587000250816345},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.5475000143051147},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.545799970626831},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.4941999912261963},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.44830000400543213},{"id":"https://openalex.org/C182335926","wikidata":"https://www.wikidata.org/wiki/Q17093020","display_name":"Kernel principal component analysis","level":4,"score":0.4350000023841858},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.41530001163482666},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.40720000863075256},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3873000144958496},{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.375900000333786},{"id":"https://openalex.org/C7545210","wikidata":"https://www.wikidata.org/wiki/Q838123","display_name":"Data redundancy","level":2,"score":0.35440000891685486},{"id":"https://openalex.org/C2776879701","wikidata":"https://www.wikidata.org/wiki/Q25048660","display_name":"Multiple kernel learning","level":4,"score":0.34599998593330383},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.33250001072883606},{"id":"https://openalex.org/C122280245","wikidata":"https://www.wikidata.org/wiki/Q620622","display_name":"Kernel method","level":3,"score":0.328000009059906},{"id":"https://openalex.org/C152139883","wikidata":"https://www.wikidata.org/wiki/Q252973","display_name":"Mutual information","level":2,"score":0.3228999972343445},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.3224000036716461},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.310699999332428},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.30889999866485596},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.3082999885082245},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2939999997615814},{"id":"https://openalex.org/C110083411","wikidata":"https://www.wikidata.org/wiki/Q1744628","display_name":"Statistical classification","level":2,"score":0.26269999146461487},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.25839999318122864},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2574999928474426},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.25450000166893005},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.2529999911785126},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.25220000743865967},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.25119999051094055}],"mesh":[],"locations_count":3,"locations":[{"id":"pmh:oai:unsworks.library.unsw.edu.au:1959.4/53507","is_oa":true,"landing_page_url":"http://hdl.handle.net/1959.4/53507","pdf_url":"https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12202/SOURCE02?view=true","source":{"id":"https://openalex.org/S4306401737","display_name":"UNSWorks (University of New South Wales, Sydney, Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I40053085","host_organization_name":"Australian Defence Force Academy","host_organization_lineage":["https://openalex.org/I40053085"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"http://purl.org/coar/resource_type/c_db06"},{"id":"pmh:oai:unsworks.unsw.edu.au:1959.4/53507","is_oa":true,"landing_page_url":"http://handle.unsw.edu.au/1959.4/53507","pdf_url":"http://handle.unsw.edu.au/1959.4/53507","source":{"id":"https://openalex.org/S4377196481","display_name":"UNSWorks (UNSW Sydney)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I31746571","host_organization_name":"UNSW Sydney","host_organization_lineage":["https://openalex.org/I31746571"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"Thesis"},{"id":"doi:10.26190/unsworks/2565","is_oa":true,"landing_page_url":"https://doi.org/10.26190/unsworks/2565","pdf_url":null,"source":{"id":"https://openalex.org/S7407053176","display_name":"University of New South Wales","issn_l":null,"issn":[],"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":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"thesis"}],"best_oa_location":{"id":"pmh:oai:unsworks.library.unsw.edu.au:1959.4/53507","is_oa":true,"landing_page_url":"http://hdl.handle.net/1959.4/53507","pdf_url":"https://unsworks.unsw.edu.au/fapi/datastream/unsworks:12202/SOURCE02?view=true","source":{"id":"https://openalex.org/S4306401737","display_name":"UNSWorks (University of New South Wales, Sydney, Australia)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I40053085","host_organization_name":"Australian Defence Force Academy","host_organization_lineage":["https://openalex.org/I40053085"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"http://purl.org/coar/resource_type/c_db06"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320313567","display_name":"University of New South Wales Canberra","ror":null},{"id":"https://openalex.org/F4320320965","display_name":"University of New South Wales","ror":"https://ror.org/03r8z3t63"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W6945869588.pdf","grobid_xml":"https://content.openalex.org/works/W6945869588.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Hyperspectral":[0],"data":[1,82],"provides":[2],"rich":[3],"information":[4,151],"and":[5,36,63,116,161,227,254,257,270,315],"is":[6,87,119,125,143,181,242,261,274,287],"very":[7],"useful":[8],"for":[9,100,251,281,295,305],"a":[10,53,91,108,148,218,246],"range":[11],"of":[12,48,70,79,95,136,174,209,220,234,239,245,248],"applications":[13],"from":[14,132],"ground-cover":[15],"types":[16],"identification":[17,168],"to":[18,66,128,156,164,216,276,293],"target":[19,166,267],"detection.":[20],"With":[21],"many":[22],"benefits":[23],"they":[24],"also":[25],"present":[26],"some":[27],"challenges":[28],"including":[29],"high":[30],"storage":[31],"cost,":[32],"intensive":[33],"computational":[34],"load":[35],"difficulties":[37],"in":[38,43,56,169,183],"machine":[39],"assisted":[40],"interpretation,":[41],"namely,":[42],"classification.":[44,104],"The":[45,84,206,236,263,318],"limited":[46],"number":[47,247],"training":[49],"samples":[50],"may":[51],"cause":[52],"significant":[54],"loss":[55],"classification":[57,328],"accuracy.":[58],"This":[59],"thesis":[60],"investigates":[61],"effective":[62],"feasible":[64],"approaches":[65,250,324],"reduce":[67],"the":[68,71,76,80,96,133,137,158,165,170,175,184,190,196,210,232,243,266,278,290,296,301,322],"dimensionality":[69],"hyperspectral":[72,102],"images":[73],"while":[74],"keeping":[75],"intrinsic":[77],"structure":[78],"input":[81,191,211,271],"intact.":[83],"first":[85,127,197],"study":[86,241],"concerned":[88],"with":[89,153],"finding":[90],"subspace":[92],"which":[93,111,222],"consists":[94],"most":[97],"informative":[98],"features":[99,131,192,212,221],"reliable":[101],"image":[103],"In":[105,187],"this":[106,188,240],"study,":[107,189],"hybrid":[109],"approach":[110,260],"combines":[112],"both":[113,313],"feature":[114,117,178],"extraction":[115,179],"selection":[118,142,253],"proposed.":[120,262],"Principal":[121],"Component":[122],"Analysis":[123],"(PCA)":[124],"applied":[126],"generate":[129],"new":[130],"complete":[134],"set":[135],"original":[138],"spectral":[139],"bands.":[140],"Feature":[141],"then":[144,214],"performed":[145],"effectively":[146],"using":[147,300],"normalized":[149],"mutual":[150],"measure":[152],"two":[154],"constraints":[155],"maximize":[157],"general":[159],"relevance":[160],"minimize":[162],"redundancy":[163],"class":[167],"selected":[171,302],"subspace.":[172],"Improvement":[173],"existing":[176],"nonlinear":[177,201],"method":[180],"undertaken":[182],"second":[185],"study.":[186],"are":[193,213],"decorrelated":[194],"at":[195,289],"step":[198],"by":[199],"applying":[200],"kernel":[202,252,268,272,279,284,298,303],"principal":[203],"component":[204],"analysis.":[205],"spatial":[207],"properties":[208],"incorporated":[215],"select":[217,277],"subset":[219],"better":[223],"reveal":[224],"object":[225],"structures":[226],"provide":[228,325],"good":[229],"separation":[230],"among":[231],"classes":[233],"interest.":[235],"third":[237],"contribution":[238],"evaluation":[244],"recent":[249],"an":[255,326],"improved":[256,327],"computationally":[258],"efficient":[259],"alignment":[264],"between":[265],"matrix":[269,273],"used":[275,288],"parameter(s)":[280,304],"each":[282,306],"candidate":[283],"function.":[285,307],"Cross-validation":[286],"final":[291],"stage":[292],"search":[294],"best":[297],"function":[299],"Experiments":[308],"were":[309],"carried":[310],"out":[311],"on":[312],"real":[314],"synthetic":[316],"data.":[317],"results":[319],"show":[320],"that":[321],"proposed":[323],"performance.":[329]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
