Enhancing temporal series of sentinel-2 and sentinel-3 data products: from classical regression to deep learning approach

Detalhes bibliográficos
Autor(a) principal: Shrestha, Anu Bhalu
Data de Publicação: 2021
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/113706
Resumo: Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
id RCAP_6273e718a6687ce0cabd250afe74f479
oai_identifier_str oai:run.unl.pt:10362/113706
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Enhancing temporal series of sentinel-2 and sentinel-3 data products: from classical regression to deep learning approachSentinel-2Sentinel-3Inter-sensor Data Products EstimationMachine LearningDeep LearningConvolutional Neural NetworkSDG 2 - Zero hungerSDG 3 - Good health and well-beingDissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesThe free and open availability of satellite images covering global extent in recent days provides many novel opportunities for global monitoring of the earth’s surface. Sentinel-2 (S2) and Sentinel-3 (S3) satellite missions capture mid to high resolution imagery with frequent revisit and show data synergy as they both focus on land and ocean observational needs. Specifically, the high temporal resolution of S3 (1-2 day revisit) presents potential in filling the data gaps in S2 (5 day revisit) vegetation products. In this scenario, this study assesses the feasibility of using Sentinel-3 images for Sentinel-2 vegetation products estimation using machine learning (ML) and deep learning (DL) approaches. This study employs four state of the art ML regression algorithms, linear regression, ridge regression, Support Vector Regression (SVR) and Random Forest Regression (RFR) and two DL network architectures with different depth and complexities, Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) to predict the S2 NDVI and SAVI maps from the S3 spectral bands information. A paired S2/S3 dataset is prepared for the study area covering one S2 tile in Extremadura, Spain. The results demonstrate that all the DL architectures except pixel-wise MLP outperformed the ML models with the 3D CNN performing the best. The best performing 3D CNN architecture obtained remarkable mean squared error (MSE) of 0.00198 for NDVI and 0.00282 for SAVI while the best performing ML algorithms were patch-wise RFR with MSE of 0.0035 in case of NDVI and patchwise SVR with MSE of 0.00586 for SAVI. The models and the dataset prepared for this study will be useful for further research that focus on capitalizing the free and open availability of Sentinel-2 and Sentinel-3 imagery as well as new and advanced technologies to provide better vegetation monitoring capabilities for our planet.Pla Bañón, FilibertoFernández-Beltrán, RubénCaetano, Mário Sílvio Rochinha de AndradeRUNShrestha, Anu Bhalu2021-03-11T15:22:45Z2021-03-052021-03-05T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/113706TID:202670902enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:56:36Zoai:run.unl.pt:10362/113706Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:42:22.208203Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Enhancing temporal series of sentinel-2 and sentinel-3 data products: from classical regression to deep learning approach
title Enhancing temporal series of sentinel-2 and sentinel-3 data products: from classical regression to deep learning approach
spellingShingle Enhancing temporal series of sentinel-2 and sentinel-3 data products: from classical regression to deep learning approach
Shrestha, Anu Bhalu
Sentinel-2
Sentinel-3
Inter-sensor Data Products Estimation
Machine Learning
Deep Learning
Convolutional Neural Network
SDG 2 - Zero hunger
SDG 3 - Good health and well-being
title_short Enhancing temporal series of sentinel-2 and sentinel-3 data products: from classical regression to deep learning approach
title_full Enhancing temporal series of sentinel-2 and sentinel-3 data products: from classical regression to deep learning approach
title_fullStr Enhancing temporal series of sentinel-2 and sentinel-3 data products: from classical regression to deep learning approach
title_full_unstemmed Enhancing temporal series of sentinel-2 and sentinel-3 data products: from classical regression to deep learning approach
title_sort Enhancing temporal series of sentinel-2 and sentinel-3 data products: from classical regression to deep learning approach
author Shrestha, Anu Bhalu
author_facet Shrestha, Anu Bhalu
author_role author
dc.contributor.none.fl_str_mv Pla Bañón, Filiberto
Fernández-Beltrán, Rubén
Caetano, Mário Sílvio Rochinha de Andrade
RUN
dc.contributor.author.fl_str_mv Shrestha, Anu Bhalu
dc.subject.por.fl_str_mv Sentinel-2
Sentinel-3
Inter-sensor Data Products Estimation
Machine Learning
Deep Learning
Convolutional Neural Network
SDG 2 - Zero hunger
SDG 3 - Good health and well-being
topic Sentinel-2
Sentinel-3
Inter-sensor Data Products Estimation
Machine Learning
Deep Learning
Convolutional Neural Network
SDG 2 - Zero hunger
SDG 3 - Good health and well-being
description Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
publishDate 2021
dc.date.none.fl_str_mv 2021-03-11T15:22:45Z
2021-03-05
2021-03-05T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/113706
TID:202670902
url http://hdl.handle.net/10362/113706
identifier_str_mv TID:202670902
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
instname_str Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron_str RCAAP
institution RCAAP
reponame_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
collection Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository.name.fl_str_mv Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
repository.mail.fl_str_mv
_version_ 1799138035102646272

Registros relacionados