{"id":"https://openalex.org/W2548198689","doi":"https://doi.org/10.1109/igarss.2016.7729627","title":"Active selection for hyperspectral data classification with submodular method","display_name":"Active selection for hyperspectral data classification with submodular method","publication_year":2016,"publication_date":"2016-07-01","ids":{"openalex":"https://openalex.org/W2548198689","doi":"https://doi.org/10.1109/igarss.2016.7729627","mag":"2548198689"},"language":"en","primary_location":{"id":"doi:10.1109/igarss.2016.7729627","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2016.7729627","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/A5100679841","display_name":"Jiming Li","orcid":"https://orcid.org/0000-0002-5570-9952"},"institutions":[{"id":"https://openalex.org/I4210108177","display_name":"Zhejiang Police College","ror":"https://ror.org/01rxaf991","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210108177"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiming Li","raw_affiliation_strings":["Zhejiang Police College, Big Data Application Lab, Hangzhou, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Zhejiang Police College, Big Data Application Lab, Hangzhou, China","institution_ids":["https://openalex.org/I4210108177"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5100679841"],"corresponding_institution_ids":["https://openalex.org/I4210108177"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.20277714,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"58","issue":null,"first_page":"2431","last_page":"2433"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10500","display_name":"Sparse and Compressive Sensing Techniques","score":0.9977999925613403,"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":0.9977999925613403,"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/T12072","display_name":"Machine Learning and Algorithms","score":0.9968000054359436,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T10720","display_name":"Complexity and Algorithms in Graphs","score":0.9962999820709229,"subfield":{"id":"https://openalex.org/subfields/1703","display_name":"Computational Theory and Mathematics"},"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/submodular-set-function","display_name":"Submodular set function","score":0.9760737419128418},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.900333821773529},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.60984867811203},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5311787128448486},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.5276307463645935},{"id":"https://openalex.org/keywords/kernel","display_name":"Kernel (algebra)","score":0.510952889919281},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5063437223434448},{"id":"https://openalex.org/keywords/scalability","display_name":"Scalability","score":0.46462780237197876},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.4615694284439087},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.43734681606292725},{"id":"https://openalex.org/keywords/optimization-problem","display_name":"Optimization problem","score":0.4218474328517914},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3891853392124176},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3702113926410675},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.32058364152908325},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.2863919734954834},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.28440284729003906},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.1906665861606598},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.11780327558517456}],"concepts":[{"id":"https://openalex.org/C178621042","wikidata":"https://www.wikidata.org/wiki/Q7631710","display_name":"Submodular set function","level":2,"score":0.9760737419128418},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.900333821773529},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.60984867811203},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5311787128448486},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.5276307463645935},{"id":"https://openalex.org/C74193536","wikidata":"https://www.wikidata.org/wiki/Q574844","display_name":"Kernel (algebra)","level":2,"score":0.510952889919281},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5063437223434448},{"id":"https://openalex.org/C48044578","wikidata":"https://www.wikidata.org/wiki/Q727490","display_name":"Scalability","level":2,"score":0.46462780237197876},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.4615694284439087},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.43734681606292725},{"id":"https://openalex.org/C137836250","wikidata":"https://www.wikidata.org/wiki/Q984063","display_name":"Optimization problem","level":2,"score":0.4218474328517914},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3891853392124176},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3702113926410675},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.32058364152908325},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.2863919734954834},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.28440284729003906},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.1906665861606598},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.11780327558517456},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss.2016.7729627","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss.2016.7729627","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/W229472839","https://openalex.org/W1563601717","https://openalex.org/W1576820654","https://openalex.org/W1680189815","https://openalex.org/W2030476695","https://openalex.org/W2041478093","https://openalex.org/W2086058123","https://openalex.org/W2139573966","https://openalex.org/W2143996311","https://openalex.org/W2165049595","https://openalex.org/W2506684654","https://openalex.org/W4301207789","https://openalex.org/W6671770434"],"related_works":["https://openalex.org/W1595919516","https://openalex.org/W2945022594","https://openalex.org/W2194604332","https://openalex.org/W4379619607","https://openalex.org/W1989453388","https://openalex.org/W2922450688","https://openalex.org/W4313349366","https://openalex.org/W3141561286","https://openalex.org/W4288560659","https://openalex.org/W2125653933"],"abstract_inverted_index":{"In":[0],"this":[1,68],"paper,":[2],"we":[3],"address":[4],"the":[5,55],"following":[6],"problem:":[7],"given":[8],"an":[9],"unlabeled":[10],"hyperspectral":[11,27],"image,":[12],"how":[13],"to":[14],"select":[15],"a":[16,36,58],"good":[17],"subset":[18],"of":[19],"samples":[20],"for":[21],"labeling":[22],"as":[23],"training":[24],"set":[25],"in":[26],"data":[28],"classification.":[29],"We":[30],"apply":[31],"submodular":[32,44],"active":[33],"selection":[34,73],"on":[35],"RBF":[37],"kernel":[38],"similarity":[39],"based":[40],"graph":[41],"through":[42],"using":[43],"function":[45],"optimization.":[46],"Submodular":[47],"functions":[48],"provide":[49],"theoretical":[50],"performance":[51],"guarantees":[52],"while":[53],"at":[54],"same":[56],"time":[57],"fast":[59],"and":[60,74],"scalable":[61],"optimization":[62],"procedure.":[63],"The":[64],"experiments":[65],"show":[66],"that":[67],"approach":[69],"outperforms":[70],"average-case":[71],"random":[72],"uncertainty":[75],"sampling":[76],"when":[77],"labelling":[78],"cost":[79],"is":[80],"constrained.":[81]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}