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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Holder Codeibi 8JMKD3MGPEW34M/46T9EHH
Identifier8JMKD3MGPEW34M/45CQCDL
Repositorysid.inpe.br/sibgrapi/2021/09.05.21.39
Last Update2021:09.05.21.39.22 (UTC) administrator
Metadata Repositorysid.inpe.br/sibgrapi/2021/09.05.21.39.22
Metadata Last Update2022:06.14.00.00.27 (UTC) administrator
DOI10.1109/SIBGRAPI54419.2021.00045
Citation KeyPereiraSant:2021:SpCoDa
TitleChessMix: Spatial Context Data Augmentation for Remote Sensing Semantic Segmentation
FormatOn-line
Year2021
Access Date2024, May 06
Number of Files1
Size2106 KiB
2. Context
Author1 Pereira, Matheus Barros
2 Santos, Jefersson Alex dos
Affiliation1 Universidade Federal de Minas Gerais 
2 Universidade Federal de Minas Gerais
EditorPaiva, Afonso
Menotti, David
Baranoski, Gladimir V. G.
Proença, Hugo Pedro
Junior, Antonio Lopes Apolinario
Papa, João Paulo
Pagliosa, Paulo
dos Santos, Thiago Oliveira
e Sá, Asla Medeiros
da Silveira, Thiago Lopes Trugillo
Brazil, Emilio Vital
Ponti, Moacir A.
Fernandes, Leandro A. F.
Avila, Sandra
e-Mail Addressmatheuspereira@dcc.ufmg.br
Conference NameConference on Graphics, Patterns and Images, 34 (SIBGRAPI)
Conference LocationGramado, RS, Brazil (virtual)
Date18-22 Oct. 2021
PublisherIEEE Computer Society
Publisher CityLos Alamitos
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2021-09-05 21:39:22 :: matheuspereira@dcc.ufmg.br -> administrator ::
2022-03-02 00:54:15 :: administrator -> menottid@gmail.com :: 2021
2022-03-02 13:34:31 :: menottid@gmail.com -> administrator :: 2021
2022-06-14 00:00:27 :: administrator -> :: 2021
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Version Typefinaldraft
Keywordsdata augmentation
semantic segmentation
remote sensing
AbstractLabeling semantic segmentation datasets is a costly and laborious process if compared with tasks like image classification and object detection. This is especially true for remote sensing applications that not only work with extremely high spatial resolution data but also commonly require the knowledge of experts of the area to perform the manual labeling. Data augmentation techniques help to improve deep learning models under the circumstance of few and imbalanced labeled samples. In this work, we propose a novel data augmentation method focused on exploring the spatial context of remote sensing semantic segmentation. This method, ChessMix, creates new synthetic images from the existing training set by mixing transformed mini-patches across the dataset in a chessboard-like grid. ChessMix prioritizes patches with more examples of the rarest classes to alleviate the imbalance problems. The results in three diverse well-known remote sensing datasets show that this is a promising approach that helps to improve the networks' performance, working especially well in datasets with few available data. The results also show that ChessMix is capable of improving the segmentation of objects with few labeled pixels when compared to the most common data augmentation methods widely used.
Arrangement 1urlib.net > SDLA > Fonds > SIBGRAPI 2021 > ChessMix: Spatial Context...
Arrangement 2urlib.net > SDLA > Fonds > Full Index > ChessMix: Spatial Context...
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/45CQCDL
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/45CQCDL
Languageen
Target File102.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/45PQ3RS
8JMKD3MGPEW34M/4742MCS
Citing Item Listsid.inpe.br/sibgrapi/2021/11.12.11.46 6
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


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