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1. Identity statement
Reference TypeJournal Article
Sitemtc-m21d.sid.inpe.br
Holder Codeisadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S
Identifier8JMKD3MGP3W34T/48N2UB5
Repositorysid.inpe.br/mtc-m21d/2023/03.14.18.14   (restricted access)
Last Update2023:03.14.18.14.59 (UTC) simone
Metadata Repositorysid.inpe.br/mtc-m21d/2023/03.14.18.14.59
Metadata Last Update2024:01.02.17.16.41 (UTC) administrator
DOI10.1109/LGRS.2023.3242430
ISSN1545-598X
Citation KeyFerrariFerrAlmeFeit:2023:FuSeSe
TitleFusing Sentinel-1 and Sentinel-2 images for deforestation detection under diverse cloud conditions
Year2023
Access Date2024, May 07
Type of Workjournal article
Secondary TypePRE PI
Number of Files1
Size1454 KiB
2. Context
Author1 Ferrari, Felipe
2 Ferreira, Matheus Pinheiro
3 Almeida, Cláudio Aparecido de
4 Feitosa, Raul Queiroz
ORCID1 0000-0003-0705-9593
2 0000-0003-0687-4422
3
4 0000-0001-8344-5096
Group1
2
3 DIPE1-COGPI-INPE-MCTI-GOV-BR
Affiliation1 Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio)
2 Instituto Militar de Engenharia (IME)
3 Instituto Nacional de Pesquisas Espaciais (INPE)
Author e-Mail Address1 ferrari@aluno.puc-rio.br
2
3 almeidaclaudio68@gmail.com
JournalIEEE Geoscience and Remote Sensing Letters
Volume20
Pagese2501005
Secondary MarkA1_GEOGRAFIA A1_ENGENHARIAS_IV A2_INTERDISCIPLINAR A2_GEOCIÊNCIAS B1_CIÊNCIA_DA_COMPUTAÇÃO B2_MATEMÁTICA_/_PROBABILIDADE_E_ESTATÍSTICA B2_BIOTECNOLOGIA B3_ASTRONOMIA_/_FÍSICA C_CIÊNCIAS_AGRÁRIAS_I
History (UTC)2023-03-14 18:14:59 :: simone -> administrator ::
2023-03-14 18:15:01 :: administrator -> simone :: 2023
2023-06-22 15:27:27 :: simone -> administrator :: 2023
2024-01-02 17:16:41 :: administrator -> simone :: 2023
3. Content and structure
Is the master or a copy?is the master
Content Stagecompleted
Transferable1
Content TypeExternal Contribution
Version Typepublisher
KeywordsAdaptive optics
Biomedical optical imaging
Clouds
Land Surface
Optical Data
Optical imaging
Optical sensors
Radar polarimetry
SAR Data
Synthetic aperture radar
Vegetation
AbstractMost current early warning systems for deforestation rely on cloud-free optical images, which are difficult to obtain in tropical regions. The fusion of optical and SAR images is an attractive alternative in these cases. Although less discriminative in cloudless regions, SAR data are nearly unaltered by clouds, allowing better discrimination in cloudy areas than the optical counterpart. This letter proposes solutions that seek the best combination between the two modalities for each pixel as a function of the surrounding cloud cover to maximize classification accuracy. We compared early, joint, and late fusion variants of Fully Convolutional Networks (FCN) to detect deforestation in the Amazon rainforest from Sentinel 1 and Sentinel 2 data. Experiments conducted to compare the architecture variants showed that optical-SAR fusion might outperform the single-modal variants for deforestation detection on pixels affected by any cloud cover level. In particular, the joint fusion approach outperformed the single modal counterparts under all cloud cover scenarios.
AreaSRE
Arrangementurlib.net > BDMCI > Fonds > COGPI > Fusing Sentinel-1 and...
doc Directory Contentaccess
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4. Conditions of access and use
Languageen
Target FileFusing_Sentinel-1_and_Sentinel-2_Images_for_Deforestation_Detection_in_the_Brazilian_Amazon_Under_Diverse_Cloud_Conditions.pdf
User Groupsimone
Reader Groupadministrator
simone
Visibilityshown
Archiving Policydenypublisher allowfinaldraft
Read Permissiondeny from all and allow from 150.163
Update Permissionnot transferred
5. Allied materials
Next Higher Units8JMKD3MGPCW/46L2FGP
Citing Item Listsid.inpe.br/bibdigital/2022/04.04.04.47 1
DisseminationWEBSCI; PORTALCAPES; SCIELO; AGU; MGA; COMPENDEX; IEEEXplore.
Host Collectionurlib.net/www/2021/06.04.03.40
6. Notes
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