{"id":"https://openalex.org/W3207069864","doi":"https://doi.org/10.1109/igarss47720.2021.9553377","title":"A Proposed Fully Constrained Least Squares for Solving Sparse Endmember Fractions with Linear Spectral Mixture Model","display_name":"A Proposed Fully Constrained Least Squares for Solving Sparse Endmember Fractions with Linear Spectral Mixture Model","publication_year":2021,"publication_date":"2021-07-11","ids":{"openalex":"https://openalex.org/W3207069864","doi":"https://doi.org/10.1109/igarss47720.2021.9553377","mag":"3207069864"},"language":"en","primary_location":{"id":"doi:10.1109/igarss47720.2021.9553377","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9553377","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","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/A5016351233","display_name":"Cuicui Ji","orcid":"https://orcid.org/0000-0001-7764-6422"},"institutions":[{"id":"https://openalex.org/I63371133","display_name":"Chongqing Jiaotong University","ror":"https://ror.org/01t001k65","country_code":"CN","type":"education","lineage":["https://openalex.org/I63371133"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Cuicui Ji","raw_affiliation_strings":["School of Civil Engineering, Chongqing Jiaotong University, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Civil Engineering, Chongqing Jiaotong University, Chongqing, China","institution_ids":["https://openalex.org/I63371133"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5056462605","display_name":"Xiaosong Li","orcid":"https://orcid.org/0000-0003-4672-1527"},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210137199","display_name":"Aerospace Information Research Institute","ror":"https://ror.org/0419fj215","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210137199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaosong Li","raw_affiliation_strings":["Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210137199","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101824873","display_name":"Jinying Wang","orcid":"https://orcid.org/0000-0003-1445-9667"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jinying Wang","raw_affiliation_strings":["Piesat Information Technology Co., Ltd., Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Piesat Information Technology Co., Ltd., Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100722210","display_name":"Maolin Chen","orcid":"https://orcid.org/0000-0001-6165-2158"},"institutions":[{"id":"https://openalex.org/I63371133","display_name":"Chongqing Jiaotong University","ror":"https://ror.org/01t001k65","country_code":"CN","type":"education","lineage":["https://openalex.org/I63371133"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Maolin Chen","raw_affiliation_strings":["School of Civil Engineering, Chongqing Jiaotong University, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Civil Engineering, Chongqing Jiaotong University, Chongqing, China","institution_ids":["https://openalex.org/I63371133"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043097022","display_name":"Jianping Pan","orcid":"https://orcid.org/0000-0003-4893-6847"},"institutions":[{"id":"https://openalex.org/I63371133","display_name":"Chongqing Jiaotong University","ror":"https://ror.org/01t001k65","country_code":"CN","type":"education","lineage":["https://openalex.org/I63371133"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianping Pan","raw_affiliation_strings":["School of Civil Engineering, Chongqing Jiaotong University, Chongqing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Civil Engineering, Chongqing Jiaotong University, Chongqing, China","institution_ids":["https://openalex.org/I63371133"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2531,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.60097266,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"4143","last_page":"4146"},"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9958999752998352,"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":0.9939000010490417,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/endmember","display_name":"Endmember","score":0.9904122352600098},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.9373341202735901},{"id":"https://openalex.org/keywords/non-negative-matrix-factorization","display_name":"Non-negative matrix factorization","score":0.7825143337249756},{"id":"https://openalex.org/keywords/least-squares-function-approximation","display_name":"Least-squares function approximation","score":0.601317822933197},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4936598241329193},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.483136385679245},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.4800868630409241},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4758845567703247},{"id":"https://openalex.org/keywords/factorization","display_name":"Factorization","score":0.46090376377105713},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.4568089544773102},{"id":"https://openalex.org/keywords/partial-least-squares-regression","display_name":"Partial least squares regression","score":0.45571067929267883},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.40446197986602783},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.39365336298942566},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.23468711972236633},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.12141388654708862}],"concepts":[{"id":"https://openalex.org/C58237817","wikidata":"https://www.wikidata.org/wiki/Q5376204","display_name":"Endmember","level":3,"score":0.9904122352600098},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9373341202735901},{"id":"https://openalex.org/C152671427","wikidata":"https://www.wikidata.org/wiki/Q10843505","display_name":"Non-negative matrix factorization","level":4,"score":0.7825143337249756},{"id":"https://openalex.org/C9936470","wikidata":"https://www.wikidata.org/wiki/Q6510405","display_name":"Least-squares function approximation","level":3,"score":0.601317822933197},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4936598241329193},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.483136385679245},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.4800868630409241},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4758845567703247},{"id":"https://openalex.org/C187834632","wikidata":"https://www.wikidata.org/wiki/Q188804","display_name":"Factorization","level":2,"score":0.46090376377105713},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4568089544773102},{"id":"https://openalex.org/C22354355","wikidata":"https://www.wikidata.org/wiki/Q422009","display_name":"Partial least squares regression","level":2,"score":0.45571067929267883},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.40446197986602783},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.39365336298942566},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.23468711972236633},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.12141388654708862},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.0},{"id":"https://openalex.org/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/igarss47720.2021.9553377","is_oa":false,"landing_page_url":"https://doi.org/10.1109/igarss47720.2021.9553377","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.5199999809265137,"id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G3904427381","display_name":null,"funder_award_id":"2020YFC1511602","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"},{"id":"https://openalex.org/G5723928317","display_name":null,"funder_award_id":"41571421,41801394","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1532610010","https://openalex.org/W1902027874","https://openalex.org/W1966798775","https://openalex.org/W2006655741","https://openalex.org/W2060384859","https://openalex.org/W2083480069","https://openalex.org/W2117741752","https://openalex.org/W2127062304","https://openalex.org/W2135029798","https://openalex.org/W2140219630","https://openalex.org/W2164535280","https://openalex.org/W2380963189","https://openalex.org/W6680012447"],"related_works":["https://openalex.org/W2089298795","https://openalex.org/W2773002387","https://openalex.org/W1980988957","https://openalex.org/W2766484909","https://openalex.org/W2802800261","https://openalex.org/W1992367803","https://openalex.org/W2163867257","https://openalex.org/W2575757533","https://openalex.org/W2811256493","https://openalex.org/W2898368675"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"propose":[4],"a":[5],"proposed":[6,62],"fully":[7,99],"constrained":[8,100],"least":[9,27,101],"squares":[10,28,102],"(p-FCLS)":[11],"for":[12],"spectral":[13,95],"unmixing,":[14],"which":[15],"assumes":[16],"that":[17,34,81],"the":[18,21,30,40,46,49,58,61,72],"endmember":[19,32],"with":[20],"absolute":[22],"maximum":[23],"negative":[24],"abundance":[25,50],"by":[26,67],"is":[29,88],"disturbing":[31],"and":[33,71,87,96,104],"can":[35],"be":[36],"ignored":[37],"subsequently,":[38],"then":[39],"other":[41],"endmembers":[42,53],"are":[43,54,76],"returned":[44],"in":[45,91],"iteration":[47],"until":[48],"of":[51,60],"all":[52],"non-negative.":[55],"To":[56],"evaluate":[57],"effectiveness":[59],"algorithm,":[63],"hyperspectral":[64,93],"data":[65],"samples":[66],"ASD":[68],"Field":[69],"spectrometer":[70],"EO-1":[73],"Hyperion":[74],"image":[75],"employed.":[77],"The":[78],"results":[79],"illustrate":[80],"p-FCLS":[82],"could":[83],"reduced":[84],"time":[85],"consumption":[86],"more":[89],"accurate":[90],"unmixing":[92],"mixed":[94],"pixel":[97],"than":[98],"(FCLS)":[103],"normalized":[105],"non-negative":[106],"matrix":[107],"factorization":[108],"(NNMF).":[109]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}