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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/3U2HNG8
Repositorysid.inpe.br/sibgrapi/2019/09.09.13.58
Last Update2019:09.19.17.19.52 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2019/09.09.13.58.40
Metadata Last Update2022:06.14.00.09.31 (UTC) administrator
DOI10.1109/SIBGRAPI.2019.00035
Citation KeyPereiraSant:2019:HoEfSu
TitleHow effective is super-resolution to improve dense labelling of coarse resolution imagery?
FormatOn-line
Year2019
Access Date2024, Apr. 28
Number of Files1
Size5351 KiB
2. Context
Author1 Pereira, Matheus Barros
2 Santos, Jefersson Alex dos
Affiliation1 Universidade Federal de Minas Gerais
2 Universidade Federal de Minas Gerais
EditorOliveira, Luciano Rebouças de
Sarder, Pinaki
Lage, Marcos
Sadlo, Filip
e-Mail Addressmatheuspereira@dcc.ufmg.br
Conference NameConference on Graphics, Patterns and Images, 32 (SIBGRAPI)
Conference LocationRio de Janeiro, RJ, Brazil
Date28-31 Oct. 2019
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2019-09-19 17:19:53 :: matheuspereira@dcc.ufmg.br -> administrator :: 2019
2022-06-14 00:09:31 :: administrator -> matheuspereira@dcc.ufmg.br :: 2019
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordssuper-resolution
semantic segmentation
remote sensing
AbstractCoarse resolution remote sensing images, such as LANDSAT and MODIS are easily found in public open repositories and, therefore, are widely used in many studies. But their use for automatic creation of thematic maps is very restrict since most of the deep-based semantic segmentation (a.k.a dense labelling) approaches are only suitable for subdecimeter data. In this paper, we design a straightforward framework in order to evaluate the effectiveness of deep-based super-resolution in the semantic segmentation of low-resolution remote sensing images. We carried out an extensive set of experiments on three remote sensing datasets with distinct nature/properties. The results show that super-resolution is effective to improve semantic segmentation performance on low-resolution aerial imagery. It not only outperforms unsupervised interpolation but also achieves semantic segmentation results comparable to high-resolution data.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2019 > How effective is...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > How effective is...
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agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/3U2HNG8
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/3U2HNG8
Languageen
Target File45.pdf
User Groupmatheuspereira@dcc.ufmg.br
Visibilityshown
Update Permissionnot transferred
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/3UA4FNL
8JMKD3MGPEW34M/3UA4FPS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2019/10.25.18.30.33 1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination edition electronicmailaddress group isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session shorttitle sponsor subject tertiarymark type url volume
7. Description control
e-Mail (login)matheuspereira@dcc.ufmg.br
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