Comparison of transformer-based and convolutional neural network-based (CNN) models for remote sensing image classification

Detalhes bibliográficos
Autor(a) principal: Belcaid, Mareyam
Data de Publicação: 2023
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/150959
Resumo: Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
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spelling Comparison of transformer-based and convolutional neural network-based (CNN) models for remote sensing image classificationCNNTransformerVision TransformerSwin Transformermultihead attentionremote sensingimage classificationDeep LearningDomínio/Área Científica::Ciências Sociais::Geografia Económica e SocialDomínio/Área Científica::Ciências Naturais::Ciências da Computação e da InformaçãoDissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial TechnologiesRemote sensing is regarded as a rich source of data that is still very valuable for mapping (classification) and monitoring of this information using various methodologies. Convolutional Neural Networks (CNNs) are frequently employed by researchers in this field as one of the key feature extraction approaches in application to satellite pictures and have achieved good performance and efficacy. Yet, they face some problems, including the overfitting problem and the need for large datasets and expensive computational resources. As a result, it would be a good idea to experiment with various Deep Learning methods, investigate them, and compare them to existing methods while taking into account all of the elements that can affect the processing. Transformers, particularly vision Transformers, have just been offered as novel Deep Learning approaches and have demonstrated good performance in a variety of domains. Hence, experimenting with these new models would be a realistic strategy in terms of learning how they behave when processing data and what value they can contribute to the picture classification area. In relation with this, the present work aims to analyze the performance of different DL methods in classifying different land cover/land use types depending on their properties and the effect of the revolution level on this. Experimental results conducted on two main datasets “EuroSAT” and “UC Merced” indicates that some CNNs, such as, “ResNET50” and “EfficientNET B0” perform well with different resolutions while for “VGG16” and the Vision Transformer, the need for a huge amount of data for the learning task is unavoidable.Meyer, HannaKnoth, ChristianTorres Sospedra, JoaquinRUNBelcaid, Mareyam2023-03-21T13:34:04Z2023-01-272023-01-27T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/150959TID:203253671enginfo: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:RCAAP2024-03-11T05:33:22Zoai:run.unl.pt:10362/150959Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:54:23.161970Repositório 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 Comparison of transformer-based and convolutional neural network-based (CNN) models for remote sensing image classification
title Comparison of transformer-based and convolutional neural network-based (CNN) models for remote sensing image classification
spellingShingle Comparison of transformer-based and convolutional neural network-based (CNN) models for remote sensing image classification
Belcaid, Mareyam
CNN
Transformer
Vision Transformer
Swin Transformer
multihead attention
remote sensing
image classification
Deep Learning
Domínio/Área Científica::Ciências Sociais::Geografia Económica e Social
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
title_short Comparison of transformer-based and convolutional neural network-based (CNN) models for remote sensing image classification
title_full Comparison of transformer-based and convolutional neural network-based (CNN) models for remote sensing image classification
title_fullStr Comparison of transformer-based and convolutional neural network-based (CNN) models for remote sensing image classification
title_full_unstemmed Comparison of transformer-based and convolutional neural network-based (CNN) models for remote sensing image classification
title_sort Comparison of transformer-based and convolutional neural network-based (CNN) models for remote sensing image classification
author Belcaid, Mareyam
author_facet Belcaid, Mareyam
author_role author
dc.contributor.none.fl_str_mv Meyer, Hanna
Knoth, Christian
Torres Sospedra, Joaquin
RUN
dc.contributor.author.fl_str_mv Belcaid, Mareyam
dc.subject.por.fl_str_mv CNN
Transformer
Vision Transformer
Swin Transformer
multihead attention
remote sensing
image classification
Deep Learning
Domínio/Área Científica::Ciências Sociais::Geografia Económica e Social
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
topic CNN
Transformer
Vision Transformer
Swin Transformer
multihead attention
remote sensing
image classification
Deep Learning
Domínio/Área Científica::Ciências Sociais::Geografia Económica e Social
Domínio/Área Científica::Ciências Naturais::Ciências da Computação e da Informação
description Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies
publishDate 2023
dc.date.none.fl_str_mv 2023-03-21T13:34:04Z
2023-01-27
2023-01-27T00:00:00Z
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/10362/150959
TID:203253671
url http://hdl.handle.net/10362/150959
identifier_str_mv TID:203253671
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
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