{"id":"https://openalex.org/W3120599847","doi":"https://doi.org/10.1109/lgrs.2020.3042199","title":"DenseNet-Based Land Cover Classification Network With Deep Fusion","display_name":"DenseNet-Based Land Cover Classification Network With Deep Fusion","publication_year":2021,"publication_date":"2021-01-13","ids":{"openalex":"https://openalex.org/W3120599847","doi":"https://doi.org/10.1109/lgrs.2020.3042199","mag":"3120599847"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2020.3042199","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2020.3042199","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/A5010380116","display_name":"Lianlei Shan","orcid":"https://orcid.org/0000-0002-4648-8246"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lianlei Shan","raw_affiliation_strings":["School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-4648-8246","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100721601","display_name":"Weiqiang Wang","orcid":"https://orcid.org/0000-0002-7185-2804"},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Weiqiang Wang","raw_affiliation_strings":["School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0002-7185-2804","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, University of Chinese Academy of Sciences, Beijing, China","institution_ids":["https://openalex.org/I4210165038"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4210165038"],"apc_list":null,"apc_paid":null,"fwci":2.86,"has_fulltext":false,"cited_by_count":47,"citation_normalized_percentile":{"value":0.90853032,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"19","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9991999864578247,"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":0.9991999864578247,"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":0.9950000047683716,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9922999739646912,"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/computer-science","display_name":"Computer science","score":0.7883608341217041},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.691990852355957},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6610379219055176},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.6116264462471008},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5953046083450317},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5169026851654053},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5168864727020264},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.5154217481613159},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.4447616636753082},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4402812421321869},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4374491572380066},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4228890836238861},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3664588928222656},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10467082262039185},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07065966725349426}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7883608341217041},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.691990852355957},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6610379219055176},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.6116264462471008},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5953046083450317},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5169026851654053},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5168864727020264},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.5154217481613159},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.4447616636753082},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4402812421321869},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4374491572380066},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4228890836238861},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3664588928222656},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10467082262039185},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07065966725349426},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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/C166957645","wikidata":"https://www.wikidata.org/wiki/Q23498","display_name":"Archaeology","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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2020.3042199","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2020.3042199","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":[{"score":0.4300000071525574,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[{"id":"https://openalex.org/G2231845458","display_name":null,"funder_award_id":"61860206004","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5170748882","display_name":null,"funder_award_id":"2018B10071","funder_id":"https://openalex.org/F4320336552","funder_display_name":"Science and Technology Innovation 2025 Major Project of Ningbo"},{"id":"https://openalex.org/G5516457395","display_name":null,"funder_award_id":"61976201","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G6400325034","display_name":null,"funder_award_id":"2017YFB1002203","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program 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},{"id":"https://openalex.org/F4320336552","display_name":"Science and Technology Innovation 2025 Major Project of Ningbo","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2111256709","https://openalex.org/W2194775991","https://openalex.org/W2504335775","https://openalex.org/W2559597482","https://openalex.org/W2560023338","https://openalex.org/W2565639579","https://openalex.org/W2598666589","https://openalex.org/W2783165089","https://openalex.org/W2787091153","https://openalex.org/W2799213142","https://openalex.org/W2804199516","https://openalex.org/W2928165649","https://openalex.org/W2962914239","https://openalex.org/W2963351448","https://openalex.org/W2963881378","https://openalex.org/W2964217532","https://openalex.org/W3004968762","https://openalex.org/W3041991648","https://openalex.org/W3105997607","https://openalex.org/W6631190155","https://openalex.org/W6638667902","https://openalex.org/W6682889407","https://openalex.org/W6741267457","https://openalex.org/W6761630670"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W4285411112","https://openalex.org/W4390516098","https://openalex.org/W2085033728","https://openalex.org/W2171299904","https://openalex.org/W2181948922","https://openalex.org/W2384362569","https://openalex.org/W4390494008","https://openalex.org/W2922442631","https://openalex.org/W2053596378"],"abstract_inverted_index":{"Recently,":[0],"fully":[1],"convolutional":[2],"network":[3,257],"(FCN)-based":[4],"(Longet":[5],"al.,":[6,289],"2015)":[7],"networks":[8,67,119],"have":[9,99],"made":[10],"impressive":[11],"success":[12],"in":[13,22,26,179,189,204,294],"semantic":[14,53],"segmentation,":[15,34,54],"and":[16,97,130,159,217,250,264,270],"these":[17],"approaches":[18],"achieve":[19,133],"satisfactory":[20],"results":[21,136],"natural":[23,46,122],"images.":[24,47,139],"However,":[25,117],"the":[27,35,49,58,64,78,118,166,169,173,180,190,205,232,243,247,252,255,267,277],"field":[28,269],"of":[29,45,52,183,212,234,246,279],"high-resolution":[30,86,138],"remote":[31,87],"sensing":[32,88],"image":[33],"accuracy":[36],"has":[37],"a":[38,106,222],"considerable":[39],"huge":[40],"gap":[41],"compared":[42],"with":[43,281],"that":[44,57,75],"Through":[48],"development":[50],"process":[51],"we":[55,141,151],"found":[56],"key":[59,79],"to":[60,80,94,112,172,230,254],"accurate":[61],"segmentation":[62,83,135,278],"is":[63,77,211,273],"context.":[65],"Effective":[66],"can":[68,260],"always":[69,92],"obtain":[70],"large":[71,95,283],"contexts,":[72],"which":[73,210,272],"means":[74],"context":[76,111],"one":[81,148,195],"successful":[82],"network.":[84],"For":[85],"images,":[89],"their":[90],"elements":[91],"extend":[93],"scope":[96],"they":[98,132],"no":[100],"clear":[101],"or":[102],"regular":[103],"boundaries.":[104],"As":[105,221],"result,":[107],"it":[108],"needs":[109],"more":[110,208],"correctly":[113],"classify":[114],"each":[115,184],"pixel.":[116],"designed":[120],"for":[121,137,215,224,276],"images":[123],"obviously":[124],"do":[125,142],"not":[126,176],"meet":[127],"this":[128],"requirement,":[129],"thus,":[131],"poor":[134],"Therefore,":[140],"some":[143],"targeted":[144],"improvements.":[145],"Based":[146],"on":[147,286],"powerful":[149],"backbone,":[150],"add":[152],"two":[153],"new":[154],"fusions":[155,200],"called":[156],"unit":[157,225],"fusion":[158,164,203,228,233,240],"cross-level":[160,227,239],"fusion,":[161,226],"respectively.":[162],"Unit":[163],"makes":[165],"connection":[167],"from":[168,242],"encoder":[170],"part":[171,175],"decoder":[174],"only":[177],"occur":[178],"final":[181],"output":[182],"dense":[185,196,236,248],"block":[186,249],"but":[187],"also":[188],"middle":[191],"feature":[192,202],"layers":[193],"inside":[194],"block.":[197],"These":[198],"added":[199],"make":[201],"same":[206],"level":[207],"complete,":[209],"great":[213],"significance":[214],"complex":[216],"zigzag":[218],"boundary":[219],"areas.":[220],"complement":[223],"aims":[229],"enhance":[231],"different":[235],"blocks.":[237],"Specifically,":[238],"learns":[241],"internal":[244],"structure":[245],"applies":[251],"design":[253],"whole":[256],"level.":[258],"It":[259],"incorporate":[261],"nonadjacent":[262],"features":[263],"rapidly":[265],"increase":[266],"receptive":[268],"context,":[271],"very":[274,282],"effective":[275],"targets":[280],"sizes.":[284],"Experiments":[285],"Deepglobe":[287],"(Demiret":[288],"2018)":[290],"show":[291],"significant":[292],"improvements":[293],"our":[295],"work.":[296]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":15},{"year":2024,"cited_by_count":11},{"year":2023,"cited_by_count":11},{"year":2022,"cited_by_count":6},{"year":2021,"cited_by_count":1}],"updated_date":"2026-07-29T09:40:50.615796","created_date":"2025-10-10T00:00:00"}