{"id":"https://openalex.org/W4200137582","doi":"https://doi.org/10.1145/3490725.3490742","title":"Multilevel and Multi-granularity of Remote Sensing Imagery Application based on Deep Learning and Machine Learning Algorithm","display_name":"Multilevel and Multi-granularity of Remote Sensing Imagery Application based on Deep Learning and Machine Learning Algorithm","publication_year":2021,"publication_date":"2021-09-17","ids":{"openalex":"https://openalex.org/W4200137582","doi":"https://doi.org/10.1145/3490725.3490742"},"language":"en","primary_location":{"id":"doi:10.1145/3490725.3490742","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3490725.3490742","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 The 4th International Conference on Machine Learning and Machine Intelligence","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/A5085330901","display_name":"Musabe Jean Bosco","orcid":"https://orcid.org/0000-0001-5417-8320"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Musabe Jean Bosco","raw_affiliation_strings":["Chongqing Key Laboratory of Computational Intelligence ,School of Computer Science and Technology, Chongqing University of Posts and Telecommunication, China"],"affiliations":[{"raw_affiliation_string":"Chongqing Key Laboratory of Computational Intelligence ,School of Computer Science and Technology, Chongqing University of Posts and Telecommunication, China","institution_ids":["https://openalex.org/I10535382"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031220156","display_name":"Guoyin Wang","orcid":"https://orcid.org/0000-0002-8521-5232"},"institutions":[{"id":"https://openalex.org/I10535382","display_name":"Chongqing University of Posts and Telecommunications","ror":"https://ror.org/03dgaqz26","country_code":"CN","type":"education","lineage":["https://openalex.org/I10535382"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guoyin Wang","raw_affiliation_strings":["Chongqing Key Laboratory of Computational Intelligence ,School of Computer Science and Technology, Chongqing University of Posts and Telecommunication, China"],"affiliations":[{"raw_affiliation_string":"Chongqing Key Laboratory of Computational Intelligence ,School of Computer Science and Technology, Chongqing University of Posts and Telecommunication, China","institution_ids":["https://openalex.org/I10535382"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5085330901"],"corresponding_institution_ids":["https://openalex.org/I10535382"],"apc_list":null,"apc_paid":null,"fwci":0.374,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.66058372,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"111","last_page":"119"},"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/T10111","display_name":"Remote Sensing in Agriculture","score":0.9926999807357788,"subfield":{"id":"https://openalex.org/subfields/2303","display_name":"Ecology"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13890","display_name":"Remote Sensing and Land Use","score":0.9455999732017517,"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/computer-science","display_name":"Computer science","score":0.8090070486068726},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7806015610694885},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6800228357315063},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6758937239646912},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.6634064316749573},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6176259517669678},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.48674583435058594},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.45397356152534485},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4384230971336365},{"id":"https://openalex.org/keywords/land-cover","display_name":"Land cover","score":0.43606263399124146},{"id":"https://openalex.org/keywords/granularity","display_name":"Granularity","score":0.43292921781539917},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.42973995208740234},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.36841392517089844},{"id":"https://openalex.org/keywords/land-use","display_name":"Land use","score":0.1357884407043457}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8090070486068726},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7806015610694885},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6800228357315063},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6758937239646912},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6634064316749573},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6176259517669678},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.48674583435058594},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.45397356152534485},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4384230971336365},{"id":"https://openalex.org/C2780648208","wikidata":"https://www.wikidata.org/wiki/Q3001793","display_name":"Land cover","level":3,"score":0.43606263399124146},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.43292921781539917},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.42973995208740234},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.36841392517089844},{"id":"https://openalex.org/C4792198","wikidata":"https://www.wikidata.org/wiki/Q1165944","display_name":"Land use","level":2,"score":0.1357884407043457},{"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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","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/C147176958","wikidata":"https://www.wikidata.org/wiki/Q77590","display_name":"Civil engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3490725.3490742","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3490725.3490742","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 The 4th International Conference on Machine Learning and Machine Intelligence","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6200000047683716,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[{"id":"https://openalex.org/G3188829757","display_name":null,"funder_award_id":"61772096","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"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1903029394","https://openalex.org/W1912954554","https://openalex.org/W1948751323","https://openalex.org/W1991360699","https://openalex.org/W2003339231","https://openalex.org/W2030869671","https://openalex.org/W2098982503","https://openalex.org/W2207849498","https://openalex.org/W2249874711","https://openalex.org/W2295936256","https://openalex.org/W2334211250","https://openalex.org/W2340017589","https://openalex.org/W2347115704","https://openalex.org/W2411876745","https://openalex.org/W2489450282","https://openalex.org/W2563369860","https://openalex.org/W2766178768","https://openalex.org/W2766564705","https://openalex.org/W2774276710","https://openalex.org/W2803946774","https://openalex.org/W2890144132","https://openalex.org/W2939569677","https://openalex.org/W2948256530","https://openalex.org/W2963881378","https://openalex.org/W2980055128","https://openalex.org/W2982394089","https://openalex.org/W2984954051","https://openalex.org/W3000486507","https://openalex.org/W3006001563","https://openalex.org/W3007923660","https://openalex.org/W3081452934","https://openalex.org/W3105228434","https://openalex.org/W4250445038"],"related_works":["https://openalex.org/W17155033","https://openalex.org/W3207760230","https://openalex.org/W1496222301","https://openalex.org/W4312814274","https://openalex.org/W1590307681","https://openalex.org/W2536018345","https://openalex.org/W4285370786","https://openalex.org/W2296488620","https://openalex.org/W2358353312","https://openalex.org/W2043913960"],"abstract_inverted_index":{"Deep":[0],"learning":[1,6,120,123,178],"classification":[2,26,126],"has":[3],"state-of-the-art":[4,167],"machine":[5,119,177],"approaches.":[7],"Earlier":[8],"work":[9],"proves":[10],"the":[11,38,42,49,77,94,117],"deep":[12,122],"convolutional":[13],"neural":[14,124,148],"network":[15,125,149],"was":[16],"successful":[17],"and":[18,27,36,47,66,82,105,121,159,168,174],"brilliant":[19],"in":[20,30,56,70],"different":[21,71],"tasks":[22],"such":[23],"as":[24,154],"image":[25,28,85,101],"processing":[29],"remote":[31,60,112],"sensing":[32,61,113],"datasets.":[33],"To":[34,75],"recognize":[35],"clarify":[37],"physical":[39],"aspect":[40],"of":[41,79,135],"earth's":[43],"surface":[44],"land":[45,50],"cover":[46],"exploit":[48],"use":[51],"is":[52,64,87],"an":[53,100,144],"exciting":[54],"issue":[55],"environmental":[57],"monitoring":[58],"analyzing":[59],"data":[62,80],"that":[63],"free":[65],"easy":[67],"to":[68,98,180],"get":[69],"areas":[72],"without":[73],"time-consuming.":[74],"improve":[76,181],"quality":[78],"sources":[81],"cooperating":[83],"with":[84,93,139,150],"representation":[86],"still":[88],"lack":[89],"methods.":[90],"We":[91,115,128,142],"started":[92],"traditional":[95,118,176],"segmentation":[96,110],"approach":[97],"divide":[99],"into":[102,108],"a":[103,130,155],"single":[104,156],"compare":[106],"multi-level":[107],"multiple":[109],"for":[111],"imagery.":[114],"review":[116],"tasks.":[127],"collected":[129],"dataset":[131],"from":[132],"six":[133],"countries":[134],"Eastern":[136],"Africa":[137],"Communities":[138],"nine":[140],"categories.":[141],"proposed":[143],"ensemble":[145],"average":[146],"model":[147,157],"three":[151],"models,":[152],"combined":[153],"trained,":[158],"achieved":[160],"96.5%":[161],"validation":[162],"accuracy.":[163,182],"Finally,":[164],"we":[165],"compared":[166],"extracted":[169],"features":[170],"pre-trained":[171],"weight":[172],"ImageNet":[173],"used":[175],"algorithms":[179]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}