Simulated multipolarized MAPSAR images to distinguish agricultural crops

Detalhes bibliográficos
Autor(a) principal: Silva, Wagner Fernando
Data de Publicação: 2012
Outros Autores: Rudorff, Bernardo Friedrich Theodor, Formaggio, Antonio Roberto, Paradella, Waldir Renato, Mura, José Claudio
Tipo de documento: Artigo
Idioma: eng
Título da fonte: Scientia Agrícola (Online)
Texto Completo: https://www.revistas.usp.br/sa/article/view/22767
Resumo: Many researchers have shown the potential of Synthetic Aperture Radar (SAR) images for agricultural applications, particularly for monitoring regions with limitations in terms of acquiring cloud free optical images. Recently, Brazil and Germany began a feasibility study on the construction of an orbital L-band SAR sensor referred to as MAPSAR (Multi-Application Purpose SAR). This sensor provides L-band images in three spatial resolutions and polarimetric, interferometric and stereoscopic capabilities. Thus, studies are needed to evaluate the potential of future MAPSAR images. The objective of this study was to evaluate multipolarized MAPSAR images simulated by the airborne SAR-R99B sensor to distinguish coffee, cotton and pasture fields in Brazil. Discrimination among crops was evaluated through graphical and cluster analysis of mean backscatter values, considering single, dual and triple polarizations. Planting row direction of coffee influenced the backscatter and was divided into two classes: parallel and perpendicular to the sensor look direction. Single polarizations had poor ability to discriminate the crops. The overall accuracies were less than 59 %, but the understanding of the microwave interaction with the crops could be explored. Combinations of two polarizations could differentiate various fields of crops, highlighting the combination VV-HV that reached 78 % overall accuracy. The use of three polarizations resulted in 85.4 % overall accuracy, indicating that the classes pasture and parallel coffee were fully discriminated from the other classes. These results confirmed the potential of multipolarized MAPSAR images to distinguish the studied crops and showed considerable improvement in the accuracy of the results when the number of polarizations was increased.
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spelling Simulated multipolarized MAPSAR images to distinguish agricultural crops SARcrop discriminationmultipolarized L-bandradarremote sensing Many researchers have shown the potential of Synthetic Aperture Radar (SAR) images for agricultural applications, particularly for monitoring regions with limitations in terms of acquiring cloud free optical images. Recently, Brazil and Germany began a feasibility study on the construction of an orbital L-band SAR sensor referred to as MAPSAR (Multi-Application Purpose SAR). This sensor provides L-band images in three spatial resolutions and polarimetric, interferometric and stereoscopic capabilities. Thus, studies are needed to evaluate the potential of future MAPSAR images. The objective of this study was to evaluate multipolarized MAPSAR images simulated by the airborne SAR-R99B sensor to distinguish coffee, cotton and pasture fields in Brazil. Discrimination among crops was evaluated through graphical and cluster analysis of mean backscatter values, considering single, dual and triple polarizations. Planting row direction of coffee influenced the backscatter and was divided into two classes: parallel and perpendicular to the sensor look direction. Single polarizations had poor ability to discriminate the crops. The overall accuracies were less than 59 %, but the understanding of the microwave interaction with the crops could be explored. Combinations of two polarizations could differentiate various fields of crops, highlighting the combination VV-HV that reached 78 % overall accuracy. The use of three polarizations resulted in 85.4 % overall accuracy, indicating that the classes pasture and parallel coffee were fully discriminated from the other classes. These results confirmed the potential of multipolarized MAPSAR images to distinguish the studied crops and showed considerable improvement in the accuracy of the results when the number of polarizations was increased. Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz2012-06-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionapplication/pdfhttps://www.revistas.usp.br/sa/article/view/2276710.1590/S0103-90162012000300005Scientia Agricola; v. 69 n. 3 (2012); 201-209Scientia Agricola; Vol. 69 Núm. 3 (2012); 201-209Scientia Agricola; Vol. 69 No. 3 (2012); 201-2091678-992X0103-9016reponame:Scientia Agrícola (Online)instname:Universidade de São Paulo (USP)instacron:USPenghttps://www.revistas.usp.br/sa/article/view/22767/24791Copyright (c) 2015 Scientia Agricolainfo:eu-repo/semantics/openAccessSilva, Wagner FernandoRudorff, Bernardo Friedrich TheodorFormaggio, Antonio RobertoParadella, Waldir RenatoMura, José Claudio2015-07-07T19:14:58Zoai:revistas.usp.br:article/22767Revistahttp://revistas.usp.br/sa/indexPUBhttps://old.scielo.br/oai/scielo-oai.phpscientia@usp.br||alleoni@usp.br1678-992X0103-9016opendoar:2015-07-07T19:14:58Scientia Agrícola (Online) - Universidade de São Paulo (USP)false
dc.title.none.fl_str_mv Simulated multipolarized MAPSAR images to distinguish agricultural crops
title Simulated multipolarized MAPSAR images to distinguish agricultural crops
spellingShingle Simulated multipolarized MAPSAR images to distinguish agricultural crops
Silva, Wagner Fernando
SAR
crop discrimination
multipolarized L-band
radar
remote sensing
title_short Simulated multipolarized MAPSAR images to distinguish agricultural crops
title_full Simulated multipolarized MAPSAR images to distinguish agricultural crops
title_fullStr Simulated multipolarized MAPSAR images to distinguish agricultural crops
title_full_unstemmed Simulated multipolarized MAPSAR images to distinguish agricultural crops
title_sort Simulated multipolarized MAPSAR images to distinguish agricultural crops
author Silva, Wagner Fernando
author_facet Silva, Wagner Fernando
Rudorff, Bernardo Friedrich Theodor
Formaggio, Antonio Roberto
Paradella, Waldir Renato
Mura, José Claudio
author_role author
author2 Rudorff, Bernardo Friedrich Theodor
Formaggio, Antonio Roberto
Paradella, Waldir Renato
Mura, José Claudio
author2_role author
author
author
author
dc.contributor.author.fl_str_mv Silva, Wagner Fernando
Rudorff, Bernardo Friedrich Theodor
Formaggio, Antonio Roberto
Paradella, Waldir Renato
Mura, José Claudio
dc.subject.por.fl_str_mv SAR
crop discrimination
multipolarized L-band
radar
remote sensing
topic SAR
crop discrimination
multipolarized L-band
radar
remote sensing
description Many researchers have shown the potential of Synthetic Aperture Radar (SAR) images for agricultural applications, particularly for monitoring regions with limitations in terms of acquiring cloud free optical images. Recently, Brazil and Germany began a feasibility study on the construction of an orbital L-band SAR sensor referred to as MAPSAR (Multi-Application Purpose SAR). This sensor provides L-band images in three spatial resolutions and polarimetric, interferometric and stereoscopic capabilities. Thus, studies are needed to evaluate the potential of future MAPSAR images. The objective of this study was to evaluate multipolarized MAPSAR images simulated by the airborne SAR-R99B sensor to distinguish coffee, cotton and pasture fields in Brazil. Discrimination among crops was evaluated through graphical and cluster analysis of mean backscatter values, considering single, dual and triple polarizations. Planting row direction of coffee influenced the backscatter and was divided into two classes: parallel and perpendicular to the sensor look direction. Single polarizations had poor ability to discriminate the crops. The overall accuracies were less than 59 %, but the understanding of the microwave interaction with the crops could be explored. Combinations of two polarizations could differentiate various fields of crops, highlighting the combination VV-HV that reached 78 % overall accuracy. The use of three polarizations resulted in 85.4 % overall accuracy, indicating that the classes pasture and parallel coffee were fully discriminated from the other classes. These results confirmed the potential of multipolarized MAPSAR images to distinguish the studied crops and showed considerable improvement in the accuracy of the results when the number of polarizations was increased.
publishDate 2012
dc.date.none.fl_str_mv 2012-06-01
dc.type.driver.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.uri.fl_str_mv https://www.revistas.usp.br/sa/article/view/22767
10.1590/S0103-90162012000300005
url https://www.revistas.usp.br/sa/article/view/22767
identifier_str_mv 10.1590/S0103-90162012000300005
dc.language.iso.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv https://www.revistas.usp.br/sa/article/view/22767/24791
dc.rights.driver.fl_str_mv Copyright (c) 2015 Scientia Agricola
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Copyright (c) 2015 Scientia Agricola
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz
publisher.none.fl_str_mv Universidade de São Paulo. Escola Superior de Agricultura Luiz de Queiroz
dc.source.none.fl_str_mv Scientia Agricola; v. 69 n. 3 (2012); 201-209
Scientia Agricola; Vol. 69 Núm. 3 (2012); 201-209
Scientia Agricola; Vol. 69 No. 3 (2012); 201-209
1678-992X
0103-9016
reponame:Scientia Agrícola (Online)
instname:Universidade de São Paulo (USP)
instacron:USP
instname_str Universidade de São Paulo (USP)
instacron_str USP
institution USP
reponame_str Scientia Agrícola (Online)
collection Scientia Agrícola (Online)
repository.name.fl_str_mv Scientia Agrícola (Online) - Universidade de São Paulo (USP)
repository.mail.fl_str_mv scientia@usp.br||alleoni@usp.br
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