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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34T/47TM2PL
Repositorysid.inpe.br/mtc-m21d/2022/11.03.13.15   (restricted access)
Last Update2022:11.03.13.15.55 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21d/2022/11.03.13.15.55
Metadata Last Update2023:01.03.16.46.22 (UTC) administrator
DOI10.3390/fi14100275
ISSN1999-5903
Citation KeyLucenaBreuKux:2022:ApLaUn
TitleThe Combined Use of UAV-Based RGB and DEM Images for the Detection and Delineation of Orange Tree Crowns with Mask R-CNN: An Approach of Labeling and Unified Framework
Year2022
MonthOct.
Access Date2024, May 27
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size4427 KiB
2. Context
Author1 Lucena, Felipe Rafael de Sá Menezes
2 Breunig, Fábio Marcelo
3 Kux, Hermann Johann Heinrich
Resume Identifier1
2
3 8JMKD3MGP5W/3C9JHCD
ORCID1
2 0000-0002-0405-9603
Group1 SER-SRE-DIPGR-INPE-MCTI-GOV-BR
2
3 DIOTG-CGCT-INPE-MCTI-GOV-BR
Affiliation1 Instituto Nacional de Pesquisas Espaciais (INPE)
2 Universidade Federal de Santa Maria (UFSM)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 felipesa01@gmail.com
2
3 arminiusbrasilis@gmail.com
JournalFuture Internet
Volume14
Number10
Pagese275
History (UTC)2022-11-03 13:16:08 :: simone -> administrator :: 2022
2023-01-03 16:46:22 :: administrator -> simone :: 2022
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
Keywordsinstance segmentation
Mask R-CNN
precision agriculture
tree delineation
tree detection
UAV-based images
AbstractIn this study, we used images obtained by Unmanned Aerial Vehicles (UAV) and an instance segmentation model based on deep learning (Mask R-CNN) to evaluate the ability to detect and delineate canopies in high density orange plantations. The main objective of the work was to evaluate the improvement acquired by the segmentation model when integrating the Canopy Height Model (CHM) as a fourth band to the images. Two models were evaluated, one with RGB images and the other with RGB + CHM images, and the results indicated that the model with combined images presents better results (overall accuracy from 90.42% to 97.01%). In addition to the comparison, this work suggests a more efficient ground truth mapping method and proposes a methodology for mosaicking the results by Mask R-CNN on remotely sensed images.
AreaSRE
Arrangement 1urlib.net > BDMCI > Fonds > Produção pgr ATUAIS > SER > The Combined Use...
Arrangement 2urlib.net > BDMCI > Fonds > Produção a partir de 2021 > CGCT > The Combined Use...
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source Directory Contentthere are no files
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4. Conditions of access and use
Languageen
Target Filefutureinternet-14-00275.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/3F3NU5S
8JMKD3MGPCW/46KUATE
Citing Item Listsid.inpe.br/bibdigital/2013/10.18.22.34 4
sid.inpe.br/bibdigital/2022/04.03.22.23 1
sid.inpe.br/mtc-m21/2012/07.13.14.49.50 1
DisseminationWEBSCI; PORTALCAPES; SCOPUS.
Host Collectionurlib.net/www/2021/06.04.03.40
6. Notes
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7. Description control
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