1. Identity statement | |
Reference Type | Conference Paper (Conference Proceedings) |
Site | sibgrapi.sid.inpe.br |
Holder Code | ibi 8JMKD3MGPEW34M/46T9EHH |
Identifier | 8JMKD3MGPEW34M/3U2HNG8 |
Repository | sid.inpe.br/sibgrapi/2019/09.09.13.58 |
Last Update | 2019:09.19.17.19.52 (UTC) administrator |
Metadata Repository | sid.inpe.br/sibgrapi/2019/09.09.13.58.40 |
Metadata Last Update | 2022:06.14.00.09.31 (UTC) administrator |
DOI | 10.1109/SIBGRAPI.2019.00035 |
Citation Key | PereiraSant:2019:HoEfSu |
Title | How effective is super-resolution to improve dense labelling of coarse resolution imagery? |
Format | On-line |
Year | 2019 |
Access Date | 2024, May 09 |
Number of Files | 1 |
Size | 5351 KiB |
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2. Context | |
Author | 1 Pereira, Matheus Barros 2 Santos, Jefersson Alex dos |
Affiliation | 1 Universidade Federal de Minas Gerais 2 Universidade Federal de Minas Gerais |
Editor | Oliveira, Luciano Rebouças de Sarder, Pinaki Lage, Marcos Sadlo, Filip |
e-Mail Address | matheuspereira@dcc.ufmg.br |
Conference Name | Conference on Graphics, Patterns and Images, 32 (SIBGRAPI) |
Conference Location | Rio de Janeiro, RJ, Brazil |
Date | 28-31 Oct. 2019 |
Publisher | IEEE Computer Society |
Publisher City | Los Alamitos |
Book Title | Proceedings |
Tertiary Type | Full 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 |
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3. Content and structure | |
Is the master or a copy? | is the master |
Content Stage | completed |
Transferable | 1 |
Version Type | finaldraft |
Keywords | super-resolution semantic segmentation remote sensing |
Abstract | Coarse 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 1 | urlib.net > SDLA > Fonds > SIBGRAPI 2019 > How effective is... |
Arrangement 2 | urlib.net > SDLA > Fonds > Full Index > How effective is... |
doc Directory Content | access |
source Directory Content | 45.pdf | 09/09/2019 10:58 | 5.2 MiB | |
agreement Directory Content | |
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4. Conditions of access and use | |
data URL | http://urlib.net/ibi/8JMKD3MGPEW34M/3U2HNG8 |
zipped data URL | http://urlib.net/zip/8JMKD3MGPEW34M/3U2HNG8 |
Language | en |
Target File | 45.pdf |
User Group | matheuspereira@dcc.ufmg.br |
Visibility | shown |
Update Permission | not transferred |
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5. Allied materials | |
Mirror Repository | sid.inpe.br/banon/2001/03.30.15.38.24 |
Next Higher Units | 8JMKD3MGPEW34M/3UA4FNL 8JMKD3MGPEW34M/3UA4FPS 8JMKD3MGPEW34M/4742MCS |
Citing Item List | sid.inpe.br/sibgrapi/2019/10.25.18.30.33 1 |
Host Collection | sid.inpe.br/banon/2001/03.30.15.38 |
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6. Notes | |
Empty Fields | archivingpolicy 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 |
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7. Description control | |
e-Mail (login) | matheuspereira@dcc.ufmg.br |
update | |
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