{"id":"https://openalex.org/W2765295621","doi":"https://doi.org/10.1109/whispers.2016.8071693","title":"Sub-pixel mapping of remotely sensed imagery based on maximum a posteriori estimation and fuzzy ARTMAP neural network","display_name":"Sub-pixel mapping of remotely sensed imagery based on maximum a posteriori estimation and fuzzy ARTMAP neural network","publication_year":2016,"publication_date":"2016-08-01","ids":{"openalex":"https://openalex.org/W2765295621","doi":"https://doi.org/10.1109/whispers.2016.8071693","mag":"2765295621"},"language":"en","primary_location":{"id":"doi:10.1109/whispers.2016.8071693","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2016.8071693","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","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/A5014326389","display_name":"Ke Wu","orcid":"https://orcid.org/0000-0001-9692-4221"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ke Wu","raw_affiliation_strings":["Institute of Geophysics and Geoinformatics, China University of Geosciences, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Institute of Geophysics and Geoinformatics, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5033017179","display_name":"Qian Du","orcid":"https://orcid.org/0000-0001-8354-7500"},"institutions":[{"id":"https://openalex.org/I99041443","display_name":"Mississippi State University","ror":"https://ror.org/0432jq872","country_code":"US","type":"education","lineage":["https://openalex.org/I4210141039","https://openalex.org/I99041443"]},{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN","US"],"is_corresponding":false,"raw_author_name":"Qian Du","raw_affiliation_strings":["Department of Electrical and Computer Engineering, Mississippi State University, Starkville, USA","Institute of Geophysics and Geoinformatics, China University of Geosciences, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"Department of Electrical and Computer Engineering, Mississippi State University, Starkville, USA","institution_ids":["https://openalex.org/I99041443"]},{"raw_affiliation_string":"Institute of Geophysics and Geoinformatics, China University of Geosciences, Wuhan, China","institution_ids":["https://openalex.org/I3124059619"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5014326389"],"corresponding_institution_ids":["https://openalex.org/I3124059619"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.3436872,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"1","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9987000226974487,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9987000226974487,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9983000159263611,"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":0.9958000183105469,"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/pixel","display_name":"Pixel","score":0.8426064252853394},{"id":"https://openalex.org/keywords/maximum-a-posteriori-estimation","display_name":"Maximum a posteriori estimation","score":0.7050934433937073},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6700647473335266},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6468890309333801},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.6402066946029663},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.625319242477417},{"id":"https://openalex.org/keywords/a-priori-and-a-posteriori","display_name":"A priori and a posteriori","score":0.6208012104034424},{"id":"https://openalex.org/keywords/fuzzy-logic","display_name":"Fuzzy logic","score":0.6139991879463196},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.6133882999420166},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.550483226776123},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5332837700843811},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4270756244659424},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.42682361602783203},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2146163284778595},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.21406373381614685},{"id":"https://openalex.org/keywords/maximum-likelihood","display_name":"Maximum likelihood","score":0.10779806971549988},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.09073489904403687},{"id":"https://openalex.org/keywords/cartography","display_name":"Cartography","score":0.07397866249084473},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.06444957852363586},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.058175355195999146}],"concepts":[{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.8426064252853394},{"id":"https://openalex.org/C9810830","wikidata":"https://www.wikidata.org/wiki/Q635384","display_name":"Maximum a posteriori estimation","level":3,"score":0.7050934433937073},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6700647473335266},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6468890309333801},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.6402066946029663},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.625319242477417},{"id":"https://openalex.org/C75553542","wikidata":"https://www.wikidata.org/wiki/Q178161","display_name":"A priori and a posteriori","level":2,"score":0.6208012104034424},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.6139991879463196},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.6133882999420166},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.550483226776123},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5332837700843811},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4270756244659424},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42682361602783203},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2146163284778595},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.21406373381614685},{"id":"https://openalex.org/C49781872","wikidata":"https://www.wikidata.org/wiki/Q1045555","display_name":"Maximum likelihood","level":2,"score":0.10779806971549988},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.09073489904403687},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.07397866249084473},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.06444957852363586},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.058175355195999146},{"id":"https://openalex.org/C111472728","wikidata":"https://www.wikidata.org/wiki/Q9471","display_name":"Epistemology","level":1,"score":0.0},{"id":"https://openalex.org/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/whispers.2016.8071693","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers.2016.8071693","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2016 8th Workshop on Hyperspectral Image and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/15","score":0.5899999737739563,"display_name":"Life in Land"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W1967329996","https://openalex.org/W1971413293","https://openalex.org/W1987779238","https://openalex.org/W2024733560","https://openalex.org/W2034104507","https://openalex.org/W2045067537","https://openalex.org/W2055448925","https://openalex.org/W2059063187","https://openalex.org/W2077756867","https://openalex.org/W2081346329","https://openalex.org/W2126739786","https://openalex.org/W2154019910","https://openalex.org/W2155550504","https://openalex.org/W2162060765","https://openalex.org/W2167903638","https://openalex.org/W2987004640","https://openalex.org/W3043538212","https://openalex.org/W6678578519","https://openalex.org/W6770257792"],"related_works":["https://openalex.org/W2150865841","https://openalex.org/W2044092692","https://openalex.org/W2088899772","https://openalex.org/W2614621130","https://openalex.org/W2547665164","https://openalex.org/W2138865713","https://openalex.org/W3103111272","https://openalex.org/W4289655544","https://openalex.org/W2169353922","https://openalex.org/W2615806692"],"abstract_inverted_index":{"Mixed":[0],"pixels":[1],"in":[2,9],"remotely":[3],"sensed":[4,137],"imagery":[5],"degrade":[6],"its":[7],"value":[8],"practical":[10],"use.":[11],"Sub-pixel":[12],"mapping":[13,126],"is":[14,49,56,61,105,131,156],"a":[15,25,94,101,116,123,134],"promising":[16],"technique":[17],"to":[18,107,121],"solve":[19],"this":[20,92],"problem.":[21],"It":[22],"can":[23,140],"generate":[24],"fine":[26],"resolution":[27,33,110],"land":[28,41,74],"cover":[29,42,75],"map":[30],"from":[31],"coarse":[32],"fractional":[34,112],"images":[35],"by":[36,70,115,133],"predicting":[37],"spatial":[38,78],"locations":[39],"of":[40,73,86,111],"classes":[43],"at":[44],"sub-pixel":[45,59,89,125],"scale.":[46],"However,":[47],"accuracy":[48],"often":[50],"limited.":[51],"When":[52],"the":[53,58,71,77,84,109,142,149,153],"scale":[54,154],"factor":[55,155],"large,":[57],"distribution":[60],"complex.":[62],"The":[63,128],"traditional":[64,150],"methods":[65],"are":[66],"carried":[67],"out":[68],"only":[69],"fractions":[72],"and":[76],"dependence":[79],"theory,":[80],"which":[81,139],"cannot":[82],"satisfy":[83],"requirement":[85],"more":[87],"accurate":[88],"mapping.":[90],"In":[91],"paper,":[93],"new":[95],"observation":[96],"model":[97,130],"based":[98],"on":[99],"maximum":[100],"posteriori":[102],"(MAP)":[103],"estimation":[104],"proposed":[106,129,143],"improve":[108],"images,":[113],"followed":[114],"fuzzy":[117],"ARTMAP":[118],"neural":[119],"network":[120],"acquire":[122],"final":[124],"result.":[127],"tested":[132],"real":[135],"remote":[136],"imagery,":[138],"confirm":[141],"method":[144],"has":[145],"better":[146],"performance":[147],"than":[148],"algorithm,":[151],"when":[152],"large.":[157]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}