Classifying Soil Type Using Radar Satellite Images

Detalhes bibliográficos
Autor(a) principal: Ahmed, MD Sajib
Data de Publicação: 2020
Outros Autores: Gonçalves, Teresa, Rato, Luis, Marques da Silva, José Rafael, Vieira, Filipe, Paixão, Luís, Salgueiro, Pedro
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10174/33858
Resumo: The growth of the crop is dependent on soil type, apart from atmospheric and geo-location characteristics. As of now, there is no direct and cost free method to measure soil property or to classify soil type. In this work, we proposed a machine learning model to classify soil type using Sentinel-1 satellite radar images. Further, the developed classifier achieved 72.17% F1-score classifying sandy, free and clayish on a set of 65003 data points collected over one year (from Oct 2018 to Sep 2019) over 14 corn parcels near Ourique, Portugal.
id RCAP_7f96699171f06188e024ef6a15718804
oai_identifier_str oai:dspace.uevora.pt:10174/33858
network_acronym_str RCAP
network_name_str Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
repository_id_str 7160
spelling Classifying Soil Type Using Radar Satellite ImagesThe growth of the crop is dependent on soil type, apart from atmospheric and geo-location characteristics. As of now, there is no direct and cost free method to measure soil property or to classify soil type. In this work, we proposed a machine learning model to classify soil type using Sentinel-1 satellite radar images. Further, the developed classifier achieved 72.17% F1-score classifying sandy, free and clayish on a set of 65003 data points collected over one year (from Oct 2018 to Sep 2019) over 14 corn parcels near Ourique, Portugal.2023-02-03T15:31:18Z2023-02-032020-01-01T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articlehttp://hdl.handle.net/10174/33858http://hdl.handle.net/10174/33858engMD Sajib Ahmed, Teresa Gonçalves, Luı́s Rato, José Rafael Marques da Silva, Filipe Vieira, Luı́s Paixão, and Pedro Salgueiro. Classifying Soil Type Using Radar Satel- lite Images. In Proceedings of the 26th Portuguese Conference on Pattern Recognition, RECPAD 2020, 2020.ndtcg@uevora.ptlmr@uevora.ptndndndndAhmed, MD SajibGonçalves, TeresaRato, LuisMarques da Silva, José RafaelVieira, FilipePaixão, LuísSalgueiro, Pedroinfo: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:RCAAP2024-01-03T19:35:57Zoai:dspace.uevora.pt:10174/33858Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T01:22:35.268441Repositório 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 Classifying Soil Type Using Radar Satellite Images
title Classifying Soil Type Using Radar Satellite Images
spellingShingle Classifying Soil Type Using Radar Satellite Images
Ahmed, MD Sajib
title_short Classifying Soil Type Using Radar Satellite Images
title_full Classifying Soil Type Using Radar Satellite Images
title_fullStr Classifying Soil Type Using Radar Satellite Images
title_full_unstemmed Classifying Soil Type Using Radar Satellite Images
title_sort Classifying Soil Type Using Radar Satellite Images
author Ahmed, MD Sajib
author_facet Ahmed, MD Sajib
Gonçalves, Teresa
Rato, Luis
Marques da Silva, José Rafael
Vieira, Filipe
Paixão, Luís
Salgueiro, Pedro
author_role author
author2 Gonçalves, Teresa
Rato, Luis
Marques da Silva, José Rafael
Vieira, Filipe
Paixão, Luís
Salgueiro, Pedro
author2_role author
author
author
author
author
author
dc.contributor.author.fl_str_mv Ahmed, MD Sajib
Gonçalves, Teresa
Rato, Luis
Marques da Silva, José Rafael
Vieira, Filipe
Paixão, Luís
Salgueiro, Pedro
description The growth of the crop is dependent on soil type, apart from atmospheric and geo-location characteristics. As of now, there is no direct and cost free method to measure soil property or to classify soil type. In this work, we proposed a machine learning model to classify soil type using Sentinel-1 satellite radar images. Further, the developed classifier achieved 72.17% F1-score classifying sandy, free and clayish on a set of 65003 data points collected over one year (from Oct 2018 to Sep 2019) over 14 corn parcels near Ourique, Portugal.
publishDate 2020
dc.date.none.fl_str_mv 2020-01-01T00:00:00Z
2023-02-03T15:31:18Z
2023-02-03
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/10174/33858
http://hdl.handle.net/10174/33858
url http://hdl.handle.net/10174/33858
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv MD Sajib Ahmed, Teresa Gonçalves, Luı́s Rato, José Rafael Marques da Silva, Filipe Vieira, Luı́s Paixão, and Pedro Salgueiro. Classifying Soil Type Using Radar Satel- lite Images. In Proceedings of the 26th Portuguese Conference on Pattern Recognition, RECPAD 2020, 2020.
nd
tcg@uevora.pt
lmr@uevora.pt
nd
nd
nd
nd
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
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_ 1799136707344334848

Registros relacionados