Multiclass Land Use and Land Cover Classification of Andean Sub-Basins in Colombia with Sentinel-2 and Deep Learning

Land Use and Land Cover (LULC) classification using remote sensing data is a challenging problem that has evolved with the update and launch of new satellites in orbit. As new satellites are launched with higher spatial and spectral resolution and shorter revisit times, LULC classification has evolv...

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Autores:
Arrechea Castillo, Darwin Alexis
Solano Correa, Yady Tatiana
Muñoz Ordóñez, Julián Fernando
Pencue Fierro, Edgar Leonairo
Figueroa Casas, Apolinar
Tipo de recurso:
Fecha de publicación:
2023
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/11850
Acceso en línea:
https://hdl.handle.net/20.500.12585/11850
Palabra clave:
Land Cover Classification
Land Use Classification
Deep Learning
Convolutional Neural Network
Remote Sensing
Sentinel-2
LEMB
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
id UTB2_3826f1cdf35bc3b3baa7cd3ca30ad9e0
oai_identifier_str oai:repositorio.utb.edu.co:20.500.12585/11850
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network_name_str Repositorio Institucional UTB
repository_id_str
dc.title.spa.fl_str_mv Multiclass Land Use and Land Cover Classification of Andean Sub-Basins in Colombia with Sentinel-2 and Deep Learning
title Multiclass Land Use and Land Cover Classification of Andean Sub-Basins in Colombia with Sentinel-2 and Deep Learning
spellingShingle Multiclass Land Use and Land Cover Classification of Andean Sub-Basins in Colombia with Sentinel-2 and Deep Learning
Land Cover Classification
Land Use Classification
Deep Learning
Convolutional Neural Network
Remote Sensing
Sentinel-2
LEMB
title_short Multiclass Land Use and Land Cover Classification of Andean Sub-Basins in Colombia with Sentinel-2 and Deep Learning
title_full Multiclass Land Use and Land Cover Classification of Andean Sub-Basins in Colombia with Sentinel-2 and Deep Learning
title_fullStr Multiclass Land Use and Land Cover Classification of Andean Sub-Basins in Colombia with Sentinel-2 and Deep Learning
title_full_unstemmed Multiclass Land Use and Land Cover Classification of Andean Sub-Basins in Colombia with Sentinel-2 and Deep Learning
title_sort Multiclass Land Use and Land Cover Classification of Andean Sub-Basins in Colombia with Sentinel-2 and Deep Learning
dc.creator.fl_str_mv Arrechea Castillo, Darwin Alexis
Solano Correa, Yady Tatiana
Muñoz Ordóñez, Julián Fernando
Pencue Fierro, Edgar Leonairo
Figueroa Casas, Apolinar
dc.contributor.author.none.fl_str_mv Arrechea Castillo, Darwin Alexis
Solano Correa, Yady Tatiana
Muñoz Ordóñez, Julián Fernando
Pencue Fierro, Edgar Leonairo
Figueroa Casas, Apolinar
dc.subject.keywords.spa.fl_str_mv Land Cover Classification
Land Use Classification
Deep Learning
Convolutional Neural Network
Remote Sensing
Sentinel-2
topic Land Cover Classification
Land Use Classification
Deep Learning
Convolutional Neural Network
Remote Sensing
Sentinel-2
LEMB
dc.subject.armarc.none.fl_str_mv LEMB
description Land Use and Land Cover (LULC) classification using remote sensing data is a challenging problem that has evolved with the update and launch of new satellites in orbit. As new satellites are launched with higher spatial and spectral resolution and shorter revisit times, LULC classification has evolved to take advantage of these improvements. However, these advancements also bring new challenges, such as the need for more sophisticated algorithms to process the increased volume and complexity of data. In recent years, deep learning techniques, such as convolutional neural networks (CNNs), have shown promising results in this area. Training deep learning models with complex architectures require cutting-edge hardware, which can be expensive and not accessible to everyone. In this study, a simple CNN based on the LeNet architecture is proposed to perform LULC classification over Sentinel-2 images. Simple CNNs such as LeNet require less computational resources compared to more-complex architectures. A total of 11 LULC classes were used for training and validating the model, which were then used for classifying the sub-basins. The analysis showed that the proposed CNN achieved an Overall Accuracy of 96.51% with a kappa coefficient of 0.962 in the validation data, outperforming traditional machine learning methods such as Random Forest, Support Vector Machine and Artificial Neural Networks, as well as state-of-the-art complex deep learning methods such as ResNet, DenseNet and EfficientNet. Moreover, despite being trained in over seven million images, it took five h to train, demonstrating that our simple CNN architecture is only effective but is also efficient.
publishDate 2023
dc.date.accessioned.none.fl_str_mv 2023-05-12T16:06:26Z
dc.date.available.none.fl_str_mv 2023-05-12T16:06:26Z
dc.date.issued.none.fl_str_mv 2023-05-11
dc.date.submitted.none.fl_str_mv 2023-05-12
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dc.identifier.citation.spa.fl_str_mv Arrechea-Castillo, D.A., Solano-Correa, Y.T., Muñoz-Ordóñez, J.F., Pencue-Fierro, E.L., Figueroa-Casas, A., 2023. Multiclass Land Use and Land Cover Classification of Andean Sub-Basins in Colombia with Sentinel-2 and Deep Learning. Remote Sensing 15, 2521. https://doi.org/10.3390/rs15102521
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12585/11850
dc.identifier.doi.none.fl_str_mv 10.3390/rs15102521
dc.identifier.instname.spa.fl_str_mv Universidad Tecnológica de Bolívar
dc.identifier.reponame.spa.fl_str_mv Repositorio Universidad Tecnológica de Bolívar
identifier_str_mv Arrechea-Castillo, D.A., Solano-Correa, Y.T., Muñoz-Ordóñez, J.F., Pencue-Fierro, E.L., Figueroa-Casas, A., 2023. Multiclass Land Use and Land Cover Classification of Andean Sub-Basins in Colombia with Sentinel-2 and Deep Learning. Remote Sensing 15, 2521. https://doi.org/10.3390/rs15102521
10.3390/rs15102521
Universidad Tecnológica de Bolívar
Repositorio Universidad Tecnológica de Bolívar
url https://hdl.handle.net/20.500.12585/11850
dc.language.iso.spa.fl_str_mv eng
language eng
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dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.cc.*.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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eu_rights_str_mv openAccess
dc.format.extent.none.fl_str_mv 20 páginas
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dc.publisher.place.spa.fl_str_mv Cartagena de Indias
dc.publisher.sede.spa.fl_str_mv Campus Tecnológico
dc.source.spa.fl_str_mv MDPI Remote Sensing - Vol. 15 No. 10 (2023)
institution Universidad Tecnológica de Bolívar
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spelling Arrechea Castillo, Darwin Alexis93ac81fe-17eb-4bd1-a2fc-f39f13cc9af2Solano Correa, Yady Tatiana64432ee7-11fa-4bfb-b643-143125ef82c1Muñoz Ordóñez, Julián Fernando78bbcb53-8019-414e-ba50-be4ad7189d43Pencue Fierro, Edgar Leonairo6964c8f9-622b-4193-9015-e2dbfaf05127Figueroa Casas, Apolinar655429a4-4738-41ad-ae58-f63c237f68ba2023-05-12T16:06:26Z2023-05-12T16:06:26Z2023-05-112023-05-12Arrechea-Castillo, D.A., Solano-Correa, Y.T., Muñoz-Ordóñez, J.F., Pencue-Fierro, E.L., Figueroa-Casas, A., 2023. Multiclass Land Use and Land Cover Classification of Andean Sub-Basins in Colombia with Sentinel-2 and Deep Learning. Remote Sensing 15, 2521. https://doi.org/10.3390/rs15102521https://hdl.handle.net/20.500.12585/1185010.3390/rs15102521Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarLand Use and Land Cover (LULC) classification using remote sensing data is a challenging problem that has evolved with the update and launch of new satellites in orbit. As new satellites are launched with higher spatial and spectral resolution and shorter revisit times, LULC classification has evolved to take advantage of these improvements. However, these advancements also bring new challenges, such as the need for more sophisticated algorithms to process the increased volume and complexity of data. In recent years, deep learning techniques, such as convolutional neural networks (CNNs), have shown promising results in this area. Training deep learning models with complex architectures require cutting-edge hardware, which can be expensive and not accessible to everyone. In this study, a simple CNN based on the LeNet architecture is proposed to perform LULC classification over Sentinel-2 images. Simple CNNs such as LeNet require less computational resources compared to more-complex architectures. A total of 11 LULC classes were used for training and validating the model, which were then used for classifying the sub-basins. The analysis showed that the proposed CNN achieved an Overall Accuracy of 96.51% with a kappa coefficient of 0.962 in the validation data, outperforming traditional machine learning methods such as Random Forest, Support Vector Machine and Artificial Neural Networks, as well as state-of-the-art complex deep learning methods such as ResNet, DenseNet and EfficientNet. Moreover, despite being trained in over seven million images, it took five h to train, demonstrating that our simple CNN architecture is only effective but is also efficient.20 páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2MDPI Remote Sensing - Vol. 15 No. 10 (2023)Multiclass Land Use and Land Cover Classification of Andean Sub-Basins in Colombia with Sentinel-2 and Deep Learninginfo:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Land Cover ClassificationLand Use ClassificationDeep LearningConvolutional Neural NetworkRemote SensingSentinel-2LEMBCartagena de IndiasCampus TecnológicoPúblico generalOswald, S.M.; Hollosi, B.; Žuvela-Aloise, M.; See, L.; Guggenberger, S.; Hafner, W.; Prokop, G.; Storch, A.; Schieder, W. 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