| 1. Identity statement | ||||
| Reference Type | Journal Article | |||
| Site | mtc-m21d.sid.inpe.br | |||
| Holder Code | isadg {BR SPINPE} ibi 8JMKD3MGPCW/3DT298S | |||
| Identifier | 8JMKD3MGP3W34T/475SMR2 | |||
| Repository | sid.inpe.br/mtc-m21d/2022/06.22.12.45 (restricted access) | |||
| Last Update | 2022:06.22.12.45.27 (UTC) simone | |||
| Metadata Repository | sid.inpe.br/mtc-m21d/2022/06.22.12.45.27 | |||
| Metadata Last Update | 2023:01.03.16.46.08 (UTC) administrator | |||
| DOI | 10.5194/isprs-archives-XLIII-B3-2022-665-2022 | |||
| ISSN | 1682-1750 | |||
| Citation Key | MartinezAdTuCoAlFe:2022:CoClRe | |||
| Title | A comparison of cloud removal methods for deforestation monitoring in Amazon rainforest  | |||
| Year | 2022 | |||
| Month | June | |||
| Access Date | 2024, May 11 | |||
| Type of Work | journal article | |||
| Secondary Type | PRE PI | |||
| Number of Files | 1 | |||
| Size | 10783 KiB | |||
| 2. Context | ||||
| Author | 1 Martinez, J. A. C. 2 Adarme, M. X. O. 3 Turnes, J. N. 4 Costa, Gilson A. O. P. 5 Almeida, Claudio Aparecido de 6 Feitosa, Raul Q. | |||
| Group | 1 2 3 4 5 DIPE1-COGPI-INPE-MCTI-GOV-BR | |||
| Affiliation | 1 Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio) 2 Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio) 3 University of Waterloo 4 Universidade do Estado do Rio de Janeiro (UERJ) 5 Instituto Nacional de Pesquisas Espaciais (INPE) 6 Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio) | |||
| Author e-Mail Address | 1 jchamorro@aluno.puc-rio.br 2 mortega@aluno.puc-rio.br 3 jnoaturn@uwaterloo.ca 4 gilson.costa@ime.uerj.br 5 claudio.almeida@inpe.br 6 raul@ele.puc-rio.br | |||
| Journal | International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences | |||
| Volume | 43 | |||
| Number | B3 | |||
| Pages | 665-671 | |||
| History (UTC) | 2022-06-22 12:46:18 :: simone -> administrator :: 2022 2022-08-29 18:41:25 :: administrator -> simone :: 2022 2022-12-19 18:53:49 :: simone -> administrator :: 2022 2023-01-03 16:46:08 :: administrator -> simone :: 2022 | |||
| 3. Content and structure | ||||
| Is the master or a copy? | is the master | |||
| Content Stage | completed | |||
| Transferable | 1 | |||
| Content Type | External Contribution | |||
| Version Type | publisher | |||
| Keywords | Cloud Removal Deep learning Deforestation Optical imagery SAR-optical Data fusion | |||
| Abstract | Deforestation in tropical rainforests is a major source of carbon dioxide emissions, an important driver of climate change. For decades, the Brazilian government has maintained monitoring programs for deforestation detection in the Brazilian Legal Amazon area based on remotely sensed optical images in a protocol that involves considerable efforts of visual interpretation. However, the Amazon region is covered with clouds for most of the year, and deforestation assessment can rely only on images acquired in the dry season when cloud-free images are more likely to capture. One possibility to lessen that restriction and enable deforestation detection throughout the year is to synthesize cloud-free optical images from corresponding SAR images, which are only marginally influenced by atmospheric conditions. This work compares a set of such image synthesis methods, considering deforestation detection in the Amazon forest as the target application. Specifically, we evaluate three deep learning methods for cloud removal in Sentinel-2 images: a conditional Generative Adversarial Network (cGAN) based on the pix2pixi architecture; an extension of that method, which uses atrous convolutions (Atrous cGANi) to enhance fine image details; and a non-generative method (DSen2-CRi) based on residual networks. In the evaluation, we assess both the quality of the generated images and the accuracy obtained when performing deforestation detection from those images. We further compare those methods with an image aggregation tool available in Google Earth Engine (GEE Tooli), which creates cloud-free mosaics from sequences of images acquired at nearby dates. In this study, we considered two sites in the Brazilian Amazon, characterized by distinct vegetation and deforestation patterns. In terms of the quality metrics and classification accuracy, the Atrous cGANi was the best performing deep learning method. The GEE Tooli outperformed all those methods when dealing with images from the dry season but turned out to be the poorest performing method in the wet season. | |||
| Area | SRE | |||
| Arrangement | urlib.net > BDMCI > Fonds > Produção a partir de 2021 > COGPI > A comparison of... | |||
| doc Directory Content | access | |||
| source Directory Content | there are no files | |||
| agreement Directory Content |
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| 4. Conditions of access and use | ||||
| Language | en | |||
| Target File | isprs-archives-XLIII-B3-2022-665-2022.pdf | |||
| User Group | simone | |||
| Reader Group | administrator simone | |||
| Visibility | shown | |||
| Read Permission | deny from all and allow from 150.163 | |||
| Update Permission | not transferred | |||
| 5. Allied materials | ||||
| Next Higher Units | 8JMKD3MGPCW/46L2FGP | |||
| Citing Item List | sid.inpe.br/bibdigital/2022/04.04.04.47 1 | |||
| Dissemination | WEBSCI; PORTALCAPES; COMPENDEX. | |||
| Host Collection | urlib.net/www/2021/06.04.03.40 | |||
| 6. Notes | ||||
| Empty Fields | alternatejournal archivingpolicy archivist callnumber copyholder copyright creatorhistory descriptionlevel e-mailaddress format isbn label lineage mark mirrorrepository nextedition notes orcid parameterlist parentrepositories previousedition previouslowerunit progress project resumeid rightsholder schedulinginformation secondarydate secondarykey secondarymark session shorttitle sponsor subject tertiarymark tertiarytype url | |||
| 7. Description control | ||||
| e-Mail (login) | simone | |||
| update | ||||