{"id":"https://openalex.org/W1963551209","doi":"https://doi.org/10.1109/lgrs.2014.2312619","title":"Active Landmark Sampling for Manifold Learning Based Spectral Unmixing","display_name":"Active Landmark Sampling for Manifold Learning Based Spectral Unmixing","publication_year":2014,"publication_date":"2014-04-08","ids":{"openalex":"https://openalex.org/W1963551209","doi":"https://doi.org/10.1109/lgrs.2014.2312619","mag":"1963551209"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2014.2312619","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2014.2312619","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-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/A5003070966","display_name":"Junhwa Chi","orcid":"https://orcid.org/0000-0003-4943-3790"},"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":true,"raw_author_name":"Junhwa Chi","raw_affiliation_strings":["School of Civil Engineering and the Laboratory for Applications of Remote Sensing, Purdue University, West Lafayette, IN, USA","Lab. for Applic. of Remote Sensing, Purdue Univ., West Lafayette, IN, USA"],"affiliations":[{"raw_affiliation_string":"School of Civil Engineering and the Laboratory for Applications of Remote Sensing, Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]},{"raw_affiliation_string":"Lab. for Applic. of Remote Sensing, Purdue Univ., West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5002083374","display_name":"Melba M. Crawford","orcid":null},"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":"Melba M. Crawford","raw_affiliation_strings":["School of Civil Engineering and the Laboratory for Applications of Remote Sensing, Purdue University, West Lafayette, IN, USA","Lab. for Applic. of Remote Sensing, Purdue Univ., West Lafayette, IN, USA"],"affiliations":[{"raw_affiliation_string":"School of Civil Engineering and the Laboratory for Applications of Remote Sensing, Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]},{"raw_affiliation_string":"Lab. for Applic. of Remote Sensing, Purdue Univ., West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5003070966"],"corresponding_institution_ids":["https://openalex.org/I219193219"],"apc_list":null,"apc_paid":null,"fwci":2.2404,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.88757835,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"11","issue":"11","first_page":"1881","last_page":"1885"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9998999834060669,"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":0.9998999834060669,"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/T12157","display_name":"Geochemistry and Geologic Mapping","score":0.9646000266075134,"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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9645000100135803,"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/landmark","display_name":"Landmark","score":0.8618825674057007},{"id":"https://openalex.org/keywords/nonlinear-dimensionality-reduction","display_name":"Nonlinear dimensionality reduction","score":0.8156062364578247},{"id":"https://openalex.org/keywords/manifold","display_name":"Manifold (fluid mechanics)","score":0.703610897064209},{"id":"https://openalex.org/keywords/manifold-alignment","display_name":"Manifold alignment","score":0.6810213327407837},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.6181719303131104},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6175006628036499},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5673776865005493},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.5195274949073792},{"id":"https://openalex.org/keywords/nonlinear-system","display_name":"Nonlinear system","score":0.4742361903190613},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4664190709590912},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4354848861694336},{"id":"https://openalex.org/keywords/overhead","display_name":"Overhead (engineering)","score":0.41104114055633545},{"id":"https://openalex.org/keywords/topology","display_name":"Topology (electrical circuits)","score":0.37241584062576294},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.36424076557159424},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.29406309127807617},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.2470359206199646},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.12891143560409546}],"concepts":[{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.8618825674057007},{"id":"https://openalex.org/C151876577","wikidata":"https://www.wikidata.org/wiki/Q7049464","display_name":"Nonlinear dimensionality reduction","level":3,"score":0.8156062364578247},{"id":"https://openalex.org/C529865628","wikidata":"https://www.wikidata.org/wiki/Q1790740","display_name":"Manifold (fluid mechanics)","level":2,"score":0.703610897064209},{"id":"https://openalex.org/C153120616","wikidata":"https://www.wikidata.org/wiki/Q17068315","display_name":"Manifold alignment","level":4,"score":0.6810213327407837},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.6181719303131104},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6175006628036499},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5673776865005493},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.5195274949073792},{"id":"https://openalex.org/C158622935","wikidata":"https://www.wikidata.org/wiki/Q660848","display_name":"Nonlinear system","level":2,"score":0.4742361903190613},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4664190709590912},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4354848861694336},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.41104114055633545},{"id":"https://openalex.org/C184720557","wikidata":"https://www.wikidata.org/wiki/Q7825049","display_name":"Topology (electrical circuits)","level":2,"score":0.37241584062576294},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.36424076557159424},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.29406309127807617},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2470359206199646},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.12891143560409546},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"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/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C78519656","wikidata":"https://www.wikidata.org/wiki/Q101333","display_name":"Mechanical engineering","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2014.2312619","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2014.2312619","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":24,"referenced_works":["https://openalex.org/W1581897071","https://openalex.org/W1988802668","https://openalex.org/W1992961908","https://openalex.org/W2001141328","https://openalex.org/W2003896345","https://openalex.org/W2005106632","https://openalex.org/W2025263547","https://openalex.org/W2053186076","https://openalex.org/W2067091528","https://openalex.org/W2070424424","https://openalex.org/W2107131609","https://openalex.org/W2107222994","https://openalex.org/W2107844881","https://openalex.org/W2111282613","https://openalex.org/W2114486983","https://openalex.org/W2138517257","https://openalex.org/W2151599207","https://openalex.org/W2153934661","https://openalex.org/W2159874418","https://openalex.org/W2168770424","https://openalex.org/W2295820431","https://openalex.org/W2903158431","https://openalex.org/W4233760599","https://openalex.org/W6683047094"],"related_works":["https://openalex.org/W2024891754","https://openalex.org/W1872343009","https://openalex.org/W4301056837","https://openalex.org/W2183457690","https://openalex.org/W2149766789","https://openalex.org/W2588117332","https://openalex.org/W2384281277","https://openalex.org/W1582938912","https://openalex.org/W3023431043","https://openalex.org/W2616045394"],"abstract_inverted_index":{"Nonlinear":[0],"manifold":[1,87,123],"learning":[2,88],"based":[3,89],"spectral":[4,90],"unmixing":[5,12,91,127],"provides":[6],"an":[7,81],"alternative":[8],"to":[9,57,118,125],"direct":[10],"nonlinear":[11,26],"methods":[13],"for":[14,40,70,86,104],"accommodating":[15],"nonlinearities":[16],"inherent":[17],"in":[18,28],"hyperspectral":[19],"data.":[20],"Although":[21],"manifolds":[22],"can":[23],"effectively":[24],"capture":[25],"features":[27],"the":[29,35,59,73,76,106,114],"dimensionality":[30],"reduction":[31],"stage":[32],"of":[33,51,64,75],"unmixing,":[34],"computational":[36,60],"overhead":[37],"is":[38,54,68],"excessive":[39],"large":[41],"remotely":[42],"sensed":[43],"data":[44],"sets.":[45],"Manifold":[46],"approximation":[47],"using":[48,92],"a":[49,93,99,120],"set":[50,97],"distinguishing":[52],"points":[53,67],"commonly":[55],"utilized":[56],"mitigate":[58],"burden,":[61],"but":[62],"selection":[63],"these":[65],"landmark":[66,83,96,110],"important":[69],"adequately":[71],"representing":[72],"topology":[74],"manifold.":[77,107],"This":[78],"study":[79],"proposes":[80],"active":[82,109],"sampling":[84,111],"framework":[85],"small":[94],"initial":[95],"and":[98,124],"computationally":[100],"efficient":[101],"backbone-based":[102],"strategy":[103,112],"constructing":[105],"The":[108],"selects":[113],"best":[115],"additional":[116],"landmarks":[117],"develop":[119],"more":[121],"representative":[122],"increase":[126],"accuracy.":[128]},"counts_by_year":[{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2014,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
