{"id":"https://openalex.org/W2900945596","doi":"https://doi.org/10.1109/igarss.2018.8519018","title":"Hyperspectral Band Selection Using Pair-Wise Constraint and Band-Wise Correlation","display_name":"Hyperspectral Band Selection Using Pair-Wise Constraint and Band-Wise Correlation","publication_year":2018,"publication_date":"2018-07-01","ids":{"openalex":"https://openalex.org/W2900945596","doi":"https://doi.org/10.1109/igarss.2018.8519018","mag":"2900945596"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2018.8519018","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2018.8519018","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-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/A5102716602","display_name":"Ting Lu","orcid":"https://orcid.org/0000-0001-8415-0185"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ting Lu","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5067097659","display_name":"Shutao Li","orcid":"https://orcid.org/0000-0002-0585-9848"},"institutions":[{"id":"https://openalex.org/I16609230","display_name":"Hunan University","ror":"https://ror.org/05htk5m33","country_code":"CN","type":"education","lineage":["https://openalex.org/I16609230"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shutao Li","raw_affiliation_strings":["College of Electrical and Information Engineering, Hunan University, Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Electrical and Information Engineering, Hunan University, Changsha, China","institution_ids":["https://openalex.org/I16609230"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5102716602"],"corresponding_institution_ids":["https://openalex.org/I16609230"],"apc_list":null,"apc_paid":null,"fwci":0.2057,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.59727875,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":"13","issue":null,"first_page":"4689","last_page":"4692"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9995999932289124,"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":0.9995999932289124,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9843000173568726,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10057","display_name":"Face and Expression Recognition","score":0.9840999841690063,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.8532578945159912},{"id":"https://openalex.org/keywords/redundancy","display_name":"Redundancy (engineering)","score":0.7216318845748901},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6883851289749146},{"id":"https://openalex.org/keywords/correlation","display_name":"Correlation","score":0.5751156806945801},{"id":"https://openalex.org/keywords/constraint","display_name":"Constraint (computer-aided design)","score":0.5722349286079407},{"id":"https://openalex.org/keywords/spectral-bands","display_name":"Spectral bands","score":0.5673999190330505},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5511615872383118},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.5332847237586975},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4664221405982971},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.4474918842315674},{"id":"https://openalex.org/keywords/principal-component-analysis","display_name":"Principal component analysis","score":0.4461961090564728},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.41530799865722656},{"id":"https://openalex.org/keywords/optics","display_name":"Optics","score":0.11317744851112366},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.1121002733707428},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.07564839720726013}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.8532578945159912},{"id":"https://openalex.org/C152124472","wikidata":"https://www.wikidata.org/wiki/Q1204361","display_name":"Redundancy (engineering)","level":2,"score":0.7216318845748901},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6883851289749146},{"id":"https://openalex.org/C117220453","wikidata":"https://www.wikidata.org/wiki/Q5172842","display_name":"Correlation","level":2,"score":0.5751156806945801},{"id":"https://openalex.org/C2776036281","wikidata":"https://www.wikidata.org/wiki/Q48769818","display_name":"Constraint (computer-aided design)","level":2,"score":0.5722349286079407},{"id":"https://openalex.org/C114700698","wikidata":"https://www.wikidata.org/wiki/Q2882278","display_name":"Spectral bands","level":2,"score":0.5673999190330505},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5511615872383118},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.5332847237586975},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4664221405982971},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.4474918842315674},{"id":"https://openalex.org/C27438332","wikidata":"https://www.wikidata.org/wiki/Q2873","display_name":"Principal component analysis","level":2,"score":0.4461961090564728},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.41530799865722656},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.11317744851112366},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.1121002733707428},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.07564839720726013},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2018.8519018","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2018.8519018","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.7099999785423279,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W140777655","https://openalex.org/W771234427","https://openalex.org/W1980007140","https://openalex.org/W1993022127","https://openalex.org/W2121410881","https://openalex.org/W2149620660","https://openalex.org/W2256782633","https://openalex.org/W2292865806","https://openalex.org/W2598259734","https://openalex.org/W2743795428"],"related_works":["https://openalex.org/W2579148721","https://openalex.org/W4387893611","https://openalex.org/W2347335694","https://openalex.org/W2091056927","https://openalex.org/W2067407580","https://openalex.org/W4317486777","https://openalex.org/W4389669152","https://openalex.org/W2038514069","https://openalex.org/W1967233468","https://openalex.org/W2009181529"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"a":[3,62,91,113],"novel":[4],"supervised":[5,87],"band":[6],"selection":[7],"(BS)":[8],"method":[9,163],"based":[10],"on":[11],"pair-wise":[12,86,153],"constraint":[13,154],"and":[14,43,59,79,140,156,173],"band-wise":[15,32,63,157],"correlation":[16,33,64,158],"information":[17,88,155],"is":[18,35,48,70,101,138],"proposed":[19,161,182],"for":[20,37,73,103,119],"the":[21,28,31,83,98,131,148,160,178,181],"dimension":[22],"reduction":[23],"of":[24,76,93,122,151,180],"hyperspectral":[25],"images.":[26],"On":[27,82],"one":[29],"hand,":[30,85],"information,":[34,159],"used":[36,71],"selecting":[38,74,104],"band-subset":[39],"with":[40,170],"lower":[41,80],"redundancy":[42],"higher":[44],"representation.":[45],"This":[46],"process":[47],"achieved":[49],"by":[50],"first":[51],"partitioning":[52],"all":[53],"spectral":[54],"bands":[55,75,115],"into":[56],"continuous":[57],"groups":[58],"then":[60],"calculate":[61],"matrix":[65],"within":[66],"each":[67,120],"group,":[68],"which":[69],"later":[72],"more":[77],"representation":[78],"redundancy.":[81],"other":[84],"(i.e.,":[89],"whether":[90],"pair":[92,121],"labeled":[94,123],"samples":[95,124,134],"are":[96,116],"from":[97,135,143],"same":[99,144],"class)":[100],"exploited":[102],"band-subsets":[105,169],"to":[106,126,128,166],"better":[107],"discriminate":[108],"different":[109,136],"classes.":[110],"That":[111],"is,":[112],"few":[114],"adaptively":[117],"chosen":[118],"according":[125],"spectral-similarity,":[127],"ensure":[129],"that":[130],"distance":[132],"between":[133],"classes":[137],"far":[139],"keep":[141],"sample-pair":[142],"class":[145],"close.":[146],"By":[147],"joint":[149],"use":[150],"both":[152],"BS":[162,183],"can":[164],"lead":[165],"select":[167],"optimal":[168],"low-redundancy,":[171],"high-representation":[172],"high-discrimination.":[174],"Experimental":[175],"results":[176],"demonstrate":[177],"effectiveness":[179],"method.":[184]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}