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1. Identity statement
Reference TypeConference Paper (Conference Proceedings)
Sitesibgrapi.sid.inpe.br
Identifier8JMKD3MGPEW34M/47JU5QE
Repositorysid.inpe.br/sibgrapi/2022/09.10.19.32
Last Update2022:09.10.19.32.31 (UTC) arbackes@yahoo.com.br
Metadata Repositorysid.inpe.br/sibgrapi/2022/09.10.19.32.31
Metadata Last Update2023:05.23.04.20.42 (UTC) administrator
DOI10.1109/SIBGRAPI55357.2022.9991781
Citation KeySilvaJúnMarEscBac:2022:NoCoUA
TitleNon-Linear co-registration in UAVs' images using deep learning
Short TitleNon-Linear co-registration in UAVs' images using deep learning
FormatOn-line
Year2022
Access Date2024, Apr. 27
Number of Files1
Size3753 KiB
2. Context
Author1 Silva, Leandro Henrique Furtado Pinto
2 Júnior, Jocival Dantas Dias
3 Mari, João Fernando
4 Escarpinati, Mauricio Cunha
5 Backes, André Ricardo
Affiliation1 School of Computer Science, Federal University of Uberlândia
2 School of Computer Science, Federal University of Uberlândia
3 Federal University of Viçosa, Campus Rio Paranaíba
4 School of Computer Science, Federal University of Uberlândia
5 School of Computer Science, Federal University of Uberlândia
e-Mail Addressarbackes@yahoo.com.br
Conference NameConference on Graphics, Patterns and Images, 35 (SIBGRAPI)
Conference LocationNatal, RN
Date24-27 Oct. 2022
Book TitleProceedings
Tertiary TypeFull Paper
History (UTC)2022-09-10 19:32:31 :: arbackes@yahoo.com.br -> administrator ::
2023-05-23 04:20:42 :: administrator -> :: 2022
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Keywordsimage registration
multispectral image
deep learning
precision agriculture
UAV
AbstractUnmanned Aerial Vehicles (UAVs) has stood out for assisting, enhancing, and optimizing agricultural production. Images captured by UAVs allow a detailed view of the analyzed region since the flight occurs at low and medium altitudes (50m to 400m). In addition, there is a wide variety of sensors (RGB cameras, heat capture sensors, multi and hyperspectral cameras, among others), each with its own characteristics and capable of producing different information. In multi-spectral images acquisition, we use a distinct sensor to capture each image band and at different time, leading to misalignments. To tackle this problem we propose to train a deep neural network to predict the vector deformation fields to perform the registration between bands of a multi-spectral image. The proposed approach has an accuracy ranging from 89.90% to 93.79% in the task of estimating the displacement field between bands. With this field estimated by the network, it is possible to register between the bands without the need for manual marking of points.
Arrangementurlib.net > SDLA > Fonds > SIBGRAPI 2022 > Non-Linear co-registration in UAVs' images using deep learning
doc Directory Contentaccess
source Directory Contentthere are no files
agreement Directory Content
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4. Conditions of access and use
data URLhttp://urlib.net/ibi/8JMKD3MGPEW34M/47JU5QE
zipped data URLhttp://urlib.net/zip/8JMKD3MGPEW34M/47JU5QE
Languageen
Target Filebackes_9.pdf
User Grouparbackes@yahoo.com.br
Visibilityshown
5. Allied materials
Mirror Repositorysid.inpe.br/banon/2001/03.30.15.38.24
Next Higher Units8JMKD3MGPEW34M/495MHJ8
Citing Item Listsid.inpe.br/sibgrapi/2023/05.19.12.10 6
sid.inpe.br/sibgrapi/2022/06.10.21.49 1
Host Collectionsid.inpe.br/banon/2001/03.30.15.38
6. Notes
Empty Fieldsarchivingpolicy archivist area callnumber contenttype copyholder copyright creatorhistory descriptionlevel dissemination documentstage edition editor electronicmailaddress group holdercode isbn issn label lineage mark nextedition notes numberofvolumes orcid organization pages parameterlist parentrepositories previousedition previouslowerunit progress project publisher publisheraddress readergroup readpermission resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark secondarytype serieseditor session sponsor subject tertiarymark type url versiontype volume


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