{"id":"https://openalex.org/W2548097957","doi":"https://doi.org/10.1109/igarss.2016.7729383","title":"Efficient semi-supervised feature selection for VHR remote sensing images","display_name":"Efficient semi-supervised feature selection for VHR remote sensing images","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2548097957","doi":"https://doi.org/10.1109/igarss.2016.7729383","mag":"2548097957"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2016.7729383","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2016.7729383","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","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/A5100329991","display_name":"Xi Chen","orcid":"https://orcid.org/0000-0002-8566-2358"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]},{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Xi Chen","raw_affiliation_strings":["Department of Forestry and Natural Resources, Purdue University, West Lafayette, USA","Harbin Institute of Technology, School of Electronics and Information Engineering, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Forestry and Natural Resources, Purdue University, West Lafayette, USA","institution_ids":["https://openalex.org/I219193219"]},{"raw_affiliation_string":"Harbin Institute of Technology, School of Electronics and Information Engineering, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109681448","display_name":"Lin Song","orcid":"https://orcid.org/0000-0003-2289-7325"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lin Song","raw_affiliation_strings":["Harbin Institute of Technology, School of Electronics and Information Engineering, Harbin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology, School of Electronics and Information Engineering, Harbin, China","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048574514","display_name":"Yuguan Hou","orcid":"https://orcid.org/0000-0002-4217-0441"},"institutions":[{"id":"https://openalex.org/I204983213","display_name":"Harbin Institute of Technology","ror":"https://ror.org/01yqg2h08","country_code":"CN","type":"education","lineage":["https://openalex.org/I204983213"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yuguan Hou","raw_affiliation_strings":["Harbin Institute of Technology, Harbin, Heilongjiang, CN"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Harbin Institute of Technology, Harbin, Heilongjiang, CN","institution_ids":["https://openalex.org/I204983213"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040724077","display_name":"Guofan Shao","orcid":"https://orcid.org/0000-0002-8572-9567"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Guofan Shao","raw_affiliation_strings":["Department of Forestry and Natural Resources, Purdue University, West Lafayette, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Forestry and Natural Resources, Purdue University, West Lafayette, USA","institution_ids":["https://openalex.org/I219193219"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"17","issue":null,"first_page":"1500","last_page":"1503"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9958000183105469,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9958000183105469,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9957000017166138,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9914000034332275,"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/computer-science","display_name":"Computer science","score":0.791678786277771},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.7454754710197449},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5921266078948975},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5270395874977112},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5096917152404785},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.467255562543869},{"id":"https://openalex.org/keywords/regularization","display_name":"Regularization (linguistics)","score":0.4653967320919037},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.4306561052799225},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3658619523048401}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.791678786277771},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.7454754710197449},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5921266078948975},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5270395874977112},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5096917152404785},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.467255562543869},{"id":"https://openalex.org/C2776135515","wikidata":"https://www.wikidata.org/wiki/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.4653967320919037},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.4306561052799225},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3658619523048401},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2016.7729383","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2016.7729383","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1963860205","https://openalex.org/W1988881859","https://openalex.org/W2027287150","https://openalex.org/W2081028792","https://openalex.org/W2083162467","https://openalex.org/W2093679105","https://openalex.org/W2126185804","https://openalex.org/W2135496738","https://openalex.org/W2145301697","https://openalex.org/W2149620660","https://openalex.org/W2158933803","https://openalex.org/W2244035566","https://openalex.org/W6681822384"],"related_works":["https://openalex.org/W4205762803","https://openalex.org/W2535856026","https://openalex.org/W2265065644","https://openalex.org/W2134699697","https://openalex.org/W3017188156","https://openalex.org/W2322875716","https://openalex.org/W3147584709","https://openalex.org/W2977677679","https://openalex.org/W4386564352","https://openalex.org/W2952668426"],"abstract_inverted_index":{"Feature":[0],"selection":[1,30,59,118],"is":[2,32,80,108],"often":[3],"required":[4],"to":[5,37,85,110],"select":[6],"a":[7,54,100,128],"feature":[8,13,29,58,122],"subset":[9],"from":[10],"the":[11,26,39,49,64,68,87,112,117],"original":[12],"set":[14],"of":[15,17,28,67,71,77,89,94,116],"objects":[16,44,79],"very":[18],"high":[19],"resolution":[20],"(VHR)":[21],"remote":[22],"sensing":[23],"images.":[24],"However,":[25],"majority":[27],"methods":[31],"supervised,":[33],"and":[34,120,137],"could":[35],"fail":[36],"identify":[38],"relevant":[40],"features":[41,90],"when":[42],"labeled":[43],"are":[45,125],"scarce.":[46],"To":[47],"address":[48],"problem,":[50],"this":[51],"paper":[52],"proposes":[53],"method,":[55],"efficient":[56],"semi-supervised":[57],"(ESFS),":[60],"by":[61],"effectively":[62],"exploiting":[63],"underlying":[65],"information":[66],"huge":[69],"amount":[70],"unlabeled":[72,78],"objects.":[73],"Firstly,":[74],"probability":[75],"matrix":[76],"utilized":[81],"in":[82,114],"loss":[83],"function":[84],"measure":[86],"relevance":[88],"on":[91,127],"classes,":[92],"instead":[93],"using":[95],"traditional":[96],"graph.":[97],"Secondly,":[98],"construction":[99],"l":[101],"<inf":[102],"xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"":[103],"xmlns:xlink=\"http://www.w3.org/1999/xlink\">2,1</inf>":[104],"-norm":[105],"regularization":[106],"term":[107],"imposed":[109],"ensure":[111],"sparsity":[113],"rows":[115],"matrix,":[119],"consequent":[121],"selection.":[123],"Experiments":[124],"carried":[126],"VHR":[129],"image":[130],"demonstrate":[131],"that":[132],"ESFS":[133],"outperforms":[134],"other":[135],"classical":[136],"latest":[138],"methods.":[139]},"counts_by_year":[{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2018,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}