Evaluation of machine learning algorithms in the classification of multispectral images from the Sentinel-2A/2B Orbital Sensor for mapping the environmental dynamics of Ria Formosa (Algarve, Portugal)
Autor(a) principal: | |
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Data de Publicação: | 2023 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.1/20050 |
Resumo: | With the growing availability of remote sensing orbital spatial data, the applications of machine learning (ML) algorithms have been leveraging the field of process automation in image classification. The present work aimed to evaluate the precision and accuracy of ML algorithms in the classification of Sentinel 2A/2B images from an area of high environmental dynamics, such as Ria Formosa (Algarve, Portugal). The images were submitted to classification by groups of ML algorithms such as the Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Decision Tree (DT). The Orfeo Toolbox (OTB) open-source programming package made the algorithms available. Ten samples were collected for each of the 14 land use and cover classes in the Ria Formosa area, totaling 140 samples. Of these, 70% were for training and 30% for validating the classification. The evaluation metrics used were the class discrimination measures: Recall (<i>R</i>), the Global Kappa Index (k), and the General Accuracy Index (OA). The results showed that the KNN and DT algorithms demonstrated a greater discrimination capacity for most classes. SVM and RF significantly improved class discrimination when using larger samples for training. Merging the classified images significantly improved the classification accuracy, ranging from 71% to 81%. This evaluation made it possible to define sets of ML algorithms sensitive to change detection for mapping and monitoring dynamic environments. |
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Evaluation of machine learning algorithms in the classification of multispectral images from the Sentinel-2A/2B Orbital Sensor for mapping the environmental dynamics of Ria Formosa (Algarve, Portugal)Remote sensingGISMachine learningImage classificationBarrier islandsEnvironmental monitoringWith the growing availability of remote sensing orbital spatial data, the applications of machine learning (ML) algorithms have been leveraging the field of process automation in image classification. The present work aimed to evaluate the precision and accuracy of ML algorithms in the classification of Sentinel 2A/2B images from an area of high environmental dynamics, such as Ria Formosa (Algarve, Portugal). The images were submitted to classification by groups of ML algorithms such as the Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Decision Tree (DT). The Orfeo Toolbox (OTB) open-source programming package made the algorithms available. Ten samples were collected for each of the 14 land use and cover classes in the Ria Formosa area, totaling 140 samples. Of these, 70% were for training and 30% for validating the classification. The evaluation metrics used were the class discrimination measures: Recall (<i>R</i>), the Global Kappa Index (k), and the General Accuracy Index (OA). The results showed that the KNN and DT algorithms demonstrated a greater discrimination capacity for most classes. SVM and RF significantly improved class discrimination when using larger samples for training. Merging the classified images significantly improved the classification accuracy, ranging from 71% to 81%. This evaluation made it possible to define sets of ML algorithms sensitive to change detection for mapping and monitoring dynamic environments.MDPISapientiaSouza, Flavo Elano Soares deRodrigues, José Inácio2023-10-13T09:45:20Z2023-09-012023-09-27T12:36:47Z2023-09-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.1/20050engISPRS International Journal of Geo-Information 12 (9): 361 (2023)10.3390/ijgi120903612220-9964info: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:RCAAP2023-10-18T02:00:33Zoai:sapientia.ualg.pt:10400.1/20050Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T20:35:53.207759Repositório 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 |
Evaluation of machine learning algorithms in the classification of multispectral images from the Sentinel-2A/2B Orbital Sensor for mapping the environmental dynamics of Ria Formosa (Algarve, Portugal) |
title |
Evaluation of machine learning algorithms in the classification of multispectral images from the Sentinel-2A/2B Orbital Sensor for mapping the environmental dynamics of Ria Formosa (Algarve, Portugal) |
spellingShingle |
Evaluation of machine learning algorithms in the classification of multispectral images from the Sentinel-2A/2B Orbital Sensor for mapping the environmental dynamics of Ria Formosa (Algarve, Portugal) Souza, Flavo Elano Soares de Remote sensing GIS Machine learning Image classification Barrier islands Environmental monitoring |
title_short |
Evaluation of machine learning algorithms in the classification of multispectral images from the Sentinel-2A/2B Orbital Sensor for mapping the environmental dynamics of Ria Formosa (Algarve, Portugal) |
title_full |
Evaluation of machine learning algorithms in the classification of multispectral images from the Sentinel-2A/2B Orbital Sensor for mapping the environmental dynamics of Ria Formosa (Algarve, Portugal) |
title_fullStr |
Evaluation of machine learning algorithms in the classification of multispectral images from the Sentinel-2A/2B Orbital Sensor for mapping the environmental dynamics of Ria Formosa (Algarve, Portugal) |
title_full_unstemmed |
Evaluation of machine learning algorithms in the classification of multispectral images from the Sentinel-2A/2B Orbital Sensor for mapping the environmental dynamics of Ria Formosa (Algarve, Portugal) |
title_sort |
Evaluation of machine learning algorithms in the classification of multispectral images from the Sentinel-2A/2B Orbital Sensor for mapping the environmental dynamics of Ria Formosa (Algarve, Portugal) |
author |
Souza, Flavo Elano Soares de |
author_facet |
Souza, Flavo Elano Soares de Rodrigues, José Inácio |
author_role |
author |
author2 |
Rodrigues, José Inácio |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Sapientia |
dc.contributor.author.fl_str_mv |
Souza, Flavo Elano Soares de Rodrigues, José Inácio |
dc.subject.por.fl_str_mv |
Remote sensing GIS Machine learning Image classification Barrier islands Environmental monitoring |
topic |
Remote sensing GIS Machine learning Image classification Barrier islands Environmental monitoring |
description |
With the growing availability of remote sensing orbital spatial data, the applications of machine learning (ML) algorithms have been leveraging the field of process automation in image classification. The present work aimed to evaluate the precision and accuracy of ML algorithms in the classification of Sentinel 2A/2B images from an area of high environmental dynamics, such as Ria Formosa (Algarve, Portugal). The images were submitted to classification by groups of ML algorithms such as the Support Vector Machine (SVM), Random Forest (RF), K-Nearest Neighbors (KNN), and Decision Tree (DT). The Orfeo Toolbox (OTB) open-source programming package made the algorithms available. Ten samples were collected for each of the 14 land use and cover classes in the Ria Formosa area, totaling 140 samples. Of these, 70% were for training and 30% for validating the classification. The evaluation metrics used were the class discrimination measures: Recall (<i>R</i>), the Global Kappa Index (k), and the General Accuracy Index (OA). The results showed that the KNN and DT algorithms demonstrated a greater discrimination capacity for most classes. SVM and RF significantly improved class discrimination when using larger samples for training. Merging the classified images significantly improved the classification accuracy, ranging from 71% to 81%. This evaluation made it possible to define sets of ML algorithms sensitive to change detection for mapping and monitoring dynamic environments. |
publishDate |
2023 |
dc.date.none.fl_str_mv |
2023-10-13T09:45:20Z 2023-09-01 2023-09-27T12:36:47Z 2023-09-01T00:00:00Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.1/20050 |
url |
http://hdl.handle.net/10400.1/20050 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
ISPRS International Journal of Geo-Information 12 (9): 361 (2023) 10.3390/ijgi12090361 2220-9964 |
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.publisher.none.fl_str_mv |
MDPI |
publisher.none.fl_str_mv |
MDPI |
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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1799133622490365952 |