{"id":"https://openalex.org/W1975558587","doi":"https://doi.org/10.1117/12.2036546","title":"On combining spectral and spatial information of hyperspectral image for camouflaged target detecting","display_name":"On combining spectral and spatial information of hyperspectral image for camouflaged target detecting","publication_year":2013,"publication_date":"2013-12-19","ids":{"openalex":"https://openalex.org/W1975558587","doi":"https://doi.org/10.1117/12.2036546","mag":"1975558587"},"language":"en","primary_location":{"id":"doi:10.1117/12.2036546","doi":"https://doi.org/10.1117/12.2036546","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2036546","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5100677200","display_name":"Wenshen Hua","orcid":"https://orcid.org/0000-0003-0875-2111","observed_orcids":["https://orcid.org/0000-0003-0875-2111"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wenshen Hua","raw_affiliation_strings":["Mechanical Engineering College (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mechanical Engineering College (China)","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039339597","display_name":"Xun Liu","orcid":"https://orcid.org/0000-0002-4805-3262","observed_orcids":["https://orcid.org/0000-0002-4805-3262"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xun Liu","raw_affiliation_strings":["Mechanical Engineering College (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mechanical Engineering College (China)","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5073134445","display_name":"Jia Yang","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jia Yang","raw_affiliation_strings":["Mechanical Engineering College (China)"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Mechanical Engineering College (China)","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.633,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.66066754,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":95},"biblio":{"volume":"9045","issue":null,"first_page":"90451A","last_page":"90451A"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9965999722480774,"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"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9965999722480774,"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.0010000000474974513,"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":0.00019999999494757503,"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-imagery","display_name":"hyperspectral imagery","score":0.6269999742507935},{"id":"https://openalex.org/keywords/unsupervised-classification","display_name":"unsupervised classification","score":0.9559999704360962},{"id":"https://openalex.org/keywords/spectral-angle-mapper","display_name":"spectral angle mapper","score":0.9240000247955322},{"id":"https://openalex.org/keywords/spatial-homogeneity","display_name":"spatial homogeneity","score":0.9419999718666077},{"id":"https://openalex.org/keywords/k-means-clustering","display_name":"K-means clustering","score":0.6600000262260437},{"id":"https://openalex.org/keywords/isodata","display_name":"ISODATA","score":0.9570000171661377},{"id":"https://openalex.org/keywords/target-background-discrimination","display_name":"target-background discrimination","score":0.3529999852180481}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9402399063110352},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6081843972206116},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5789932012557983},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5642403364181519},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.48264747858047485},{"id":"https://openalex.org/C78660771","wikidata":"https://www.wikidata.org/wiki/Q5508206","display_name":"Full spectral imaging","level":3,"score":0.4268674850463867},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.3830328583717346},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3628622889518738},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.193372905254364}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1117/12.2036546","doi":"https://doi.org/10.1117/12.2036546","is_oa":false,"landing_page_url":"https://doi.org/10.1117/12.2036546","pdf_url":null,"source":{"id":"https://openalex.org/S183492911","display_name":"Proceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE","issn_l":"0277-786X","issn":["0277-786X","1996-756X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","norway-1"],"host_organization":"https://openalex.org/P4310315543","host_organization_name":"SPIE","host_organization_lineage":["https://openalex.org/P4310315543"],"host_organization_lineage_names":["SPIE"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"SPIE Proceedings","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":3,"referenced_works":["https://openalex.org/W2019338222","https://openalex.org/W2366484741","https://openalex.org/W2385837511"],"related_works":["https://openalex.org/W2155835446","https://openalex.org/W1051111590","https://openalex.org/W2042939691","https://openalex.org/W4410456740","https://openalex.org/W7096729576","https://openalex.org/W4411171456","https://openalex.org/W263906867","https://openalex.org/W4294861705","https://openalex.org/W3021375626","https://openalex.org/W2975298473"],"abstract_inverted_index":{"Detecting":[0],"enemy\u2019s":[1],"targets":[2,31,202,211,240],"and":[3,20,32,87,112,161,184,192,199,208,227],"being":[4],"undetectable":[5],"play":[6],"increasingly":[7],"important":[8],"roles":[9],"in":[10,27,221],"modern":[11],"warfare.":[12],"Hyperspectral":[13],"images":[14,53],"can":[15],"provide":[16],"large":[17],"spectral":[18,22,85,111,162,187],"range":[19],"high":[21,96],"resolution,":[23],"which":[24,40,99],"are":[25,165,203,212],"invaluable":[26],"discriminating":[28],"between":[29],"camouflaged":[30,56,69,104,210,239],"backgrounds.":[33],"As":[34],"supervised":[35],"classification":[36,46],"requires":[37],"prior":[38],"knowledge":[39],"cannot":[41],"be":[42],"acquired":[43],"easily,":[44],"unsupervised":[45,123,136],"usually":[47],"is":[48,115,141,179,197,236],"adopted":[49,142],"to":[50,54,81,117,143,148,167,238],"process":[51],"hyperspectral":[52,78,93,146,174],"detect":[55],"target.":[57],"But":[58],"one":[59],"of":[60,77,172,223],"its":[61,66],"drawbacks\u2014low":[62],"detecting":[63,120,224],"accuracy":[64,121],"confines":[65],"application":[67],"for":[68,103],"target":[70,105],"detecting.":[71,106,241],"Most":[72],"research":[73],"on":[74,84],"the":[75,119,131,145,155,169,173,195,206,216,233],"processing":[76],"image":[79,94,147],"tends":[80],"focus":[82],"exclusively":[83],"domain":[86],"ignores":[88],"spatial":[89,97,113,170,177,182],"domain.":[90],"However":[91],"current":[92],"provides":[95],"resolution":[98],"contains":[100],"useful":[101],"information":[102,114],"A":[107],"new":[108,234],"method":[109,126,196,235],"combining":[110],"proposed":[116,217],"increase":[118],"using":[122],"classification.":[124],"The":[125,176],"has":[127],"two":[128],"steps.":[129],"In":[130],"first":[132],"step,":[133,157],"a":[134,158],"traditional":[135],"classifier":[137],"(i.e.":[138],"K-MEANS,":[139],"ISODATA)":[140],"classify":[144],"acquire":[149],"basic":[150],"classifications":[151],"or":[152],"clusters.":[153],"During":[154],"second":[156],"3\u00d73":[159],"model":[160],"angle":[163,188],"mapping":[164],"utilized":[166],"test":[168],"character":[171,178],"image.":[175],"defined":[180],"as":[181],"homogeneity":[183],"calculated":[185],"by":[186],"mapping.":[189],"Theory":[190],"analysis":[191],"experiment":[193],"shows":[194],"reasonable":[198],"efficient.":[200],"Camouflaged":[201],"extracted":[204],"from":[205],"background":[207],"different":[209],"also":[213],"recognized.":[214],"And":[215],"algorithm":[218],"outperforms":[219],"K-MEANS":[220],"terms":[222],"accuracy,":[225],"robustness":[226],"edge\u2019s":[228],"distinction.":[229],"This":[230],"paper":[231],"demonstrates":[232],"meaningful":[237]},"counts_by_year":[{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2016-06-24T00:00:00"}