{"id":"https://openalex.org/W3003394660","doi":"https://doi.org/10.1109/tgrs.2020.2964675","title":"Semantic Segmentation of Large-Size VHR Remote Sensing Images Using a Two-Stage Multiscale Training Architecture","display_name":"Semantic Segmentation of Large-Size VHR Remote Sensing Images Using a Two-Stage Multiscale Training Architecture","publication_year":2020,"publication_date":"2020-02-02","ids":{"openalex":"https://openalex.org/W3003394660","doi":"https://doi.org/10.1109/tgrs.2020.2964675","mag":"3003394660"},"language":"en","primary_location":{"id":"doi:10.1109/tgrs.2020.2964675","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2020.2964675","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8970467","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5088242752","display_name":"Lei Ding","orcid":"https://orcid.org/0000-0003-0653-8373"},"institutions":[{"id":"https://openalex.org/I193223587","display_name":"University of Trento","ror":"https://ror.org/05trd4x28","country_code":"IT","type":"education","lineage":["https://openalex.org/I193223587"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Lei Ding","raw_affiliation_strings":["Department of Information Engineering and Computer Science, University of Trento, Trento, Italy"],"raw_orcid":"https://orcid.org/0000-0003-0653-8373","affiliations":[{"raw_affiliation_string":"Department of Information Engineering and Computer Science, University of Trento, Trento, Italy","institution_ids":["https://openalex.org/I193223587"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032383728","display_name":"Jing Zhang","orcid":"https://orcid.org/0000-0003-1290-0738"},"institutions":[{"id":"https://openalex.org/I37796252","display_name":"Beijing University of Technology","ror":"https://ror.org/037b1pp87","country_code":"CN","type":"education","lineage":["https://openalex.org/I37796252"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jing Zhang","raw_affiliation_strings":["Department of Information, Beijing University of Technology, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Information, Beijing University of Technology, Beijing, China","institution_ids":["https://openalex.org/I37796252"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5006095323","display_name":"Lorenzo Bruzzone","orcid":"https://orcid.org/0000-0002-6036-459X"},"institutions":[{"id":"https://openalex.org/I193223587","display_name":"University of Trento","ror":"https://ror.org/05trd4x28","country_code":"IT","type":"education","lineage":["https://openalex.org/I193223587"]}],"countries":["IT"],"is_corresponding":false,"raw_author_name":"Lorenzo Bruzzone","raw_affiliation_strings":["Department of Information Engineering and Computer Science, University of Trento, Trento, Italy"],"raw_orcid":"https://orcid.org/0000-0002-6036-459X","affiliations":[{"raw_affiliation_string":"Department of Information Engineering and Computer Science, University of Trento, Trento, Italy","institution_ids":["https://openalex.org/I193223587"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":10.894,"has_fulltext":false,"cited_by_count":151,"citation_normalized_percentile":{"value":0.98640496,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":"58","issue":"8","first_page":"5367","last_page":"5376"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9994999766349792,"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":0.9994999766349792,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9987000226974487,"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"}},{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8254321813583374},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6201146841049194},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6109613180160522},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6022918820381165},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5413316488265991},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.5309721231460571},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.511881947517395},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.49452707171440125},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.49154528975486755},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.4797431230545044},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4789291322231293},{"id":"https://openalex.org/keywords/image-resolution","display_name":"Image resolution","score":0.47109800577163696},{"id":"https://openalex.org/keywords/stage","display_name":"Stage (stratigraphy)","score":0.4557986855506897},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4529615044593811},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4055618643760681},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.11919692158699036},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07744082808494568}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8254321813583374},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6201146841049194},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6109613180160522},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6022918820381165},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5413316488265991},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.5309721231460571},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.511881947517395},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.49452707171440125},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.49154528975486755},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.4797431230545044},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4789291322231293},{"id":"https://openalex.org/C205372480","wikidata":"https://www.wikidata.org/wiki/Q210521","display_name":"Image resolution","level":2,"score":0.47109800577163696},{"id":"https://openalex.org/C146357865","wikidata":"https://www.wikidata.org/wiki/Q1123245","display_name":"Stage (stratigraphy)","level":2,"score":0.4557986855506897},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4529615044593811},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4055618643760681},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.11919692158699036},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07744082808494568},{"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/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/tgrs.2020.2964675","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tgrs.2020.2964675","pdf_url":null,"source":{"id":"https://openalex.org/S111326731","display_name":"IEEE Transactions on Geoscience and Remote Sensing","issn_l":"0196-2892","issn":["0196-2892","1558-0644"],"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 Transactions on Geoscience and Remote Sensing","raw_type":"journal-article"},{"id":"pmh:oai:iris.unitn.it:11572/287650","is_oa":true,"landing_page_url":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8970467","pdf_url":null,"source":{"id":"https://openalex.org/S4377196320","display_name":"Iris (University of Trento)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I193223587","host_organization_name":"University of Trento","host_organization_lineage":["https://openalex.org/I193223587"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"}],"best_oa_location":{"id":"pmh:oai:iris.unitn.it:11572/287650","is_oa":true,"landing_page_url":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8970467","pdf_url":null,"source":{"id":"https://openalex.org/S4377196320","display_name":"Iris (University of Trento)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I193223587","host_organization_name":"University of Trento","host_organization_lineage":["https://openalex.org/I193223587"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"info:eu-repo/semantics/article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G5510044309","display_name":null,"funder_award_id":"201703170123","funder_id":"https://openalex.org/F4320322725","funder_display_name":"China Scholarship Council"}],"funders":[{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":60,"referenced_works":["https://openalex.org/W1522301498","https://openalex.org/W1686810756","https://openalex.org/W1745334888","https://openalex.org/W1799946925","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1909515874","https://openalex.org/W1912954554","https://openalex.org/W1923697677","https://openalex.org/W2097117768","https://openalex.org/W2162915993","https://openalex.org/W2163605009","https://openalex.org/W2179352600","https://openalex.org/W2186094539","https://openalex.org/W2194775991","https://openalex.org/W2337429362","https://openalex.org/W2412782625","https://openalex.org/W2525293101","https://openalex.org/W2560023338","https://openalex.org/W2563705555","https://openalex.org/W2598551616","https://openalex.org/W2598666589","https://openalex.org/W2615237590","https://openalex.org/W2618943282","https://openalex.org/W2630837129","https://openalex.org/W2683784395","https://openalex.org/W2737129951","https://openalex.org/W2789919692","https://openalex.org/W2793268137","https://openalex.org/W2796232579","https://openalex.org/W2886397424","https://openalex.org/W2888738931","https://openalex.org/W2899771611","https://openalex.org/W2902190145","https://openalex.org/W2906217354","https://openalex.org/W2939571759","https://openalex.org/W2962835968","https://openalex.org/W2962850830","https://openalex.org/W2963446712","https://openalex.org/W2963659230","https://openalex.org/W2963727650","https://openalex.org/W2963815618","https://openalex.org/W2963840672","https://openalex.org/W2963881378","https://openalex.org/W2963995737","https://openalex.org/W2964041906","https://openalex.org/W2964121744","https://openalex.org/W2964309882","https://openalex.org/W3098722327","https://openalex.org/W3100521496","https://openalex.org/W6631190155","https://openalex.org/W6637373629","https://openalex.org/W6639824700","https://openalex.org/W6640295612","https://openalex.org/W6684191040","https://openalex.org/W6696085341","https://openalex.org/W6739684510","https://openalex.org/W6739696289","https://openalex.org/W6748481559","https://openalex.org/W6756040250"],"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/W2353836703"],"abstract_inverted_index":{"Very-high":[0],"resolution":[1],"(VHR)":[2],"remote":[3],"sensing":[4],"images":[5,16,34,119],"(RSIs)":[6],"have":[7,57],"significantly":[8],"larger":[9],"spatial":[10],"size":[11],"compared":[12],"to":[13,27,62,69,91,113,136,150,158],"typical":[14],"natural":[15],"used":[17,40],"in":[18,174],"computer":[19],"vision":[20],"applications.":[21],"Therefore,":[22],"it":[23,169],"is":[24,89,111,134,148],"computationally":[25],"unaffordable":[26],"train":[28],"and":[29,49,179],"test":[30],"classifiers":[31],"on":[32,51,163],"these":[33],"at":[35],"a":[36,79,83,107,121,129],"full-size":[37],"scale.":[38],"Commonly":[39],"methodologies":[41],"for":[42],"semantic":[43,93,108],"segmentation":[44,94],"of":[45,60,95,103,176],"RSIs":[46],"perform":[47],"training":[48,86,105,127,146,172],"prediction":[50],"cropped":[52,141],"image":[53,142],"patches.":[54,143],"Thus,":[55],"they":[56],"the":[58,72,92,100,104,125],"limitation":[59],"failing":[61],"incorporate":[63],"enough":[64],"context":[65],"information.":[66],"In":[67,99,124],"order":[68],"better":[70],"exploit":[71],"correlations":[73],"between":[74],"ground":[75],"objects,":[76],"we":[77],"propose":[78],"deep":[80],"architecture":[81],"with":[82],"two-stage":[84],"multiscale":[85],"strategy":[87,147],"that":[88,168],"tailored":[90],"large-size":[96],"VHR":[97],"RSIs.":[98],"first":[101],"stage":[102],"strategy,":[106],"embedding":[109],"network":[110,133],"designed":[112,135],"learn":[114],"high-level":[115],"features":[116],"from":[117,140,155],"downscaled":[118],"covering":[120],"large":[122],"area.":[123],"second":[126],"stage,":[128],"local":[130],"feature":[131],"extraction":[132],"introduce":[137],"low-level":[138],"information":[139,153],"The":[144],"resulting":[145],"able":[149],"fuse":[151],"complementary":[152],"learned":[154],"multiple":[156],"levels":[157],"make":[159],"predictions.":[160],"Experimental":[161],"results":[162],"two":[164],"data":[165],"sets":[166],"show":[167],"outperforms":[170],"local-patch-based":[171],"models":[173],"terms":[175],"both":[177],"accuracy":[178],"stability.":[180]},"counts_by_year":[{"year":2026,"cited_by_count":5},{"year":2025,"cited_by_count":21},{"year":2024,"cited_by_count":27},{"year":2023,"cited_by_count":32},{"year":2022,"cited_by_count":33},{"year":2021,"cited_by_count":27},{"year":2020,"cited_by_count":6}],"updated_date":"2026-08-12T21:12:35.861297","created_date":"2025-10-10T00:00:00"}
