{"id":"https://openalex.org/W2889969718","doi":"https://doi.org/10.1109/aipr.2017.8457961","title":"Trading spatial resolution for improved accuracy in remote sensing imagery: an empirical study using synthetic data","display_name":"Trading spatial resolution for improved accuracy in remote sensing imagery: an empirical study using synthetic data","publication_year":2017,"publication_date":"2017-10-01","ids":{"openalex":"https://openalex.org/W2889969718","doi":"https://doi.org/10.1109/aipr.2017.8457961","mag":"2889969718"},"language":"en","primary_location":{"id":"doi:10.1109/aipr.2017.8457961","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr.2017.8457961","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","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/A5007239332","display_name":"Jordan M. Malof","orcid":"https://orcid.org/0000-0002-7851-4920"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jordan M. Malof","raw_affiliation_strings":["Department of Electrical & Computer Engineering, Duke University, Durham, NC"],"affiliations":[{"raw_affiliation_string":"Department of Electrical & Computer Engineering, Duke University, Durham, NC","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079847233","display_name":"Sravya Chelikani","orcid":null},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sravya Chelikani","raw_affiliation_strings":["Department of Electrical & Computer Engineering, Duke University, Durham, NC"],"affiliations":[{"raw_affiliation_string":"Department of Electrical & Computer Engineering, Duke University, Durham, NC","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004578492","display_name":"Leslie M. Collins","orcid":"https://orcid.org/0000-0002-9043-6531"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Leslie M. Collins","raw_affiliation_strings":["Department of Electrical & Computer Engineering, Duke University, Durham, NC"],"affiliations":[{"raw_affiliation_string":"Department of Electrical & Computer Engineering, Duke University, Durham, NC","institution_ids":["https://openalex.org/I170897317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054438743","display_name":"Kyle Bradbury","orcid":"https://orcid.org/0000-0001-9847-0243"},"institutions":[{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]},{"id":"https://openalex.org/I36194607","display_name":"Duke Energy (United States)","ror":"https://ror.org/030r69d22","country_code":"US","type":"company","lineage":["https://openalex.org/I36194607"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Kyle Bradbury","raw_affiliation_strings":["Energy Initiative, Duke University, Durham, NC"],"affiliations":[{"raw_affiliation_string":"Energy Initiative, Duke University, Durham, NC","institution_ids":["https://openalex.org/I36194607","https://openalex.org/I170897317"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5007239332"],"corresponding_institution_ids":["https://openalex.org/I170897317"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.30446487,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13282","display_name":"Automated Road and Building Extraction","score":0.9907000064849854,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9907000064849854,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean Engineering"},"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":0.988099992275238,"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/T11871","display_name":"Advanced Statistical Methods and Models","score":0.9707000255584717,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7064851522445679},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.6011516451835632},{"id":"https://openalex.org/keywords/statistic","display_name":"Statistic","score":0.5996639728546143},{"id":"https://openalex.org/keywords/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.5884665250778198},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.523735761642456},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.49000802636146545},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4485350251197815},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3756353259086609},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.15660643577575684},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.13318338990211487},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11353477835655212},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.09591692686080933}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7064851522445679},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.6011516451835632},{"id":"https://openalex.org/C89128539","wikidata":"https://www.wikidata.org/wiki/Q1949963","display_name":"Statistic","level":2,"score":0.5996639728546143},{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.5884665250778198},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.523735761642456},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.49000802636146545},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4485350251197815},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3756353259086609},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.15660643577575684},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.13318338990211487},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11353477835655212},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.09591692686080933}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/aipr.2017.8457961","is_oa":false,"landing_page_url":"https://doi.org/10.1109/aipr.2017.8457961","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE Applied Imagery Pattern Recognition Workshop (AIPR)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306318","display_name":"Duke Energy","ror":"https://ror.org/030r69d22"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2009233209","https://openalex.org/W2082396838","https://openalex.org/W2108940052","https://openalex.org/W2120327182","https://openalex.org/W2121864252","https://openalex.org/W2123175289","https://openalex.org/W2126092053","https://openalex.org/W2129905273","https://openalex.org/W2145983039","https://openalex.org/W2150123015","https://openalex.org/W2323432174","https://openalex.org/W2494424066","https://openalex.org/W2771439171","https://openalex.org/W2937970997","https://openalex.org/W4233014035","https://openalex.org/W6670912297","https://openalex.org/W6677824185","https://openalex.org/W6678328823","https://openalex.org/W6746609293"],"related_works":["https://openalex.org/W2072166414","https://openalex.org/W3209970181","https://openalex.org/W2060875994","https://openalex.org/W3034375524","https://openalex.org/W4230131218","https://openalex.org/W2070598848","https://openalex.org/W2317401237","https://openalex.org/W1990800631","https://openalex.org/W2167120702","https://openalex.org/W2579567122"],"abstract_inverted_index":{"We":[0,86],"consider":[1],"the":[2,47,75,88,103,106,110,124,129,140],"problem":[3,31],"of":[4,43,78,90,105,128],"detecting":[5],"objects":[6],"(such":[7],"as":[8,96],"trees,":[9],"rooftops,":[10],"roads,":[11],"or":[12,22,40],"cars)":[13],"in":[14,46],"remote":[15],"sensing":[16],"data":[17],"including,":[18],"for":[19,49,64,72],"example,":[20],"color":[21],"hyperspectral":[23],"imagery.":[24],"Many":[25],"detection":[26,107],"algorithms":[27],"applied":[28],"to":[29,38],"this":[30,53],"operate":[32],"by":[33],"assigning":[34],"a":[35,41,57],"decision":[36,80],"statistic":[37],"all,":[39],"subset,":[42],"spatial":[44,84,111],"locations":[45],"imagery":[48],"classification":[50,76],"purposes.":[51],"In":[52],"work":[54],"we":[55,97],"investigate":[56],"recently":[58],"proposed":[59],"method,":[60],"called":[61],"Local":[62],"Averaging":[63],"Improved":[65],"Predictions":[66],"(LAIP),":[67],"which":[68,114,143],"can":[69],"be":[70],"used":[71],"trading":[73],"off":[74],"accuracy":[77],"detector":[79,130],"statistics":[81],"with":[82],"their":[83],"precision.":[85],"explore":[87],"behaviors":[89],"LAIP":[91,115,144],"on":[92],"controlled":[93],"synthetic":[94],"data,":[95],"vary":[98],"several":[99],"experimental":[100],"conditions:":[101],"(a)":[102],"difficulty":[104],"problem,":[108],"(b)":[109],"area":[112],"over":[113],"is":[116,145],"applied,":[117],"and":[118],"(c)":[119],"how":[120],"it":[121],"behaves":[122],"when":[123],"estimated":[125],"ROC":[126],"curve":[127],"becomes":[131],"increasingly":[132],"inaccurate.":[133],"These":[134],"results":[135],"provide":[136],"basic":[137],"insights":[138],"about":[139],"conditions":[141],"under":[142],"effective.":[146]},"counts_by_year":[{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}