Assessment of multi - date sentinel - 1 polarizations and GLCM texture features capacity for onion and sunflower classification in an irrigated valley: An object level approach

Autores
Caballero, Gabriel; Platzech, Gabriel; Pezzola, Nestor Alejandro; Casella, Alejandra An; Winschel, Cristina Ines; Silva, Samanta; Ludueña, Emilia; Pasqualotto, Nieves; Delegido, Jesús
Año de publicación
2020
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The objective of this work is to evaluate the capacity of the C-band Synthetic Aperture Radar (SAR) time series imagery, acquired by the European satellite Sentinel-1 (S1), for the agriculture crop classification and its reliability to differentiate onion from sunflower, among others. The work then focused on classifying land cover in intensively cultivated agricultural regions. The study was developed in the Bonaerense Valley of the Colorado River (BVCR), Buenos Aires Province in Argentina, backed up by the field truth of 1634 field samples. In addition to the onion and sunflower crops, there are other crops present in the study area such as cereals, alfalfa, potatoes and maize, which are considered as the image background in the classification process. The field samples database was used for training and supporting a supervised classification with two machine learning algorithms—Random Forest (RF) and Support Vector Machine (SVM)—obtaining high levels of accuracy in each case. Different S1 SAR time-series features were used to assess the performance of S1 crop classification in terms of polarization VH+VV, Grey Level Co-occurrence Matrix (GLCM) image texture and a combination of both. The analysis of SAR data and their features was carried out at OBIA lot level (Object Based Image Analysis) showing an optimal strategy to counteract the effect of the residual and inherent speckle noise of the radar signal. In the process of differentiating onion and sunflower crops, the analysis of the VH+VV stack with the SVM algorithm delivered the best statistical classification results in terms of Overall Accuracy (OA) and Kappa Index, (Kp) when other crops (image background) were not considered (OA = 95.35%, Kp = 0.89). Certainly, the GLCM texture analysis derived from the S1 SAR images is a valuable source of information for obtaining very good classification results. When differentiating sunflower from onion considering also other crops present in the BVCR, the GLCM stack proved to be the most suitable dataset analyzed in this work (OA = 89.98%, Kp = 0.66 for SVM algorithm). This working methodology is applicable to other irrigated valleys in Argentina dedicated to intensive crops. There are also variables inherent to each lot, soil, crop and agricultural producer that differ according to the study area and that should be considered for each case in the future.
Fil: Caballero, Gabriel. Universidad Blas Pascal. Centro de Investigación y Desarrollo Aplicado en Informática y Telecomunicaciones (CIADE-IT); Argentina
Fil: Platzech, Gabriel. INVAP. Government & Security Division; Argentina
Fil: Pezzola, Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina
Fil: Casella, Alejandra. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina
Fil: Winschel, Cristina Ines. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina
Fil: Silva, Samanta. Ministerio de Desarrollo Agrario (Buenos Aires, provincia). Colorado River Development Corporation (CORFO); Argentina
Fil: Ludueña, Emilia. INGTRADUCCIONES; Argentina
Fil: Pasqualotto, Nieves. Universidad de Valencia. Image Processing Laboratory (IPL); España
Fil: Delegido, Jesús. Universidad de Valencia. Image Processing Laboratory (IPL); España
Fuente
Agronomy 10 (6) : 845 (2020)
Materia
Land Cover
Onions
Cobertura de Suelos
Helianthus annuus
Cebolla
Sentinel 1
Sunflower
Supervised Classification
Centinela 1
Girasol
Clasificación Supervisada
Río Colorado, Buenos Aires
Nivel de accesibilidad
acceso abierto
Condiciones de uso
http://creativecommons.org/licenses/by-nc-sa/4.0/
Repositorio
INTA Digital (INTA)
Institución
Instituto Nacional de Tecnología Agropecuaria
OAI Identificador
oai:localhost:20.500.12123/8132
Acceder
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oai_identifier_str oai:localhost:20.500.12123/8132
network_acronym_str INTADig
repository_id_str l
network_name_str INTA Digital (INTA)
spelling Assessment of multi - date sentinel - 1 polarizations and GLCM texture features capacity for onion and sunflower classification in an irrigated valley: An object level approachCaballero, GabrielPlatzech, GabrielPezzola, Nestor AlejandroCasella, Alejandra AnWinschel, Cristina InesSilva, SamantaLudueña, EmiliaPasqualotto, NievesDelegido, JesúsLand CoverOnionsCobertura de SuelosHelianthus annuusCebollaSentinel 1SunflowerSupervised ClassificationCentinela 1GirasolClasificación SupervisadaRío Colorado, Buenos AiresThe objective of this work is to evaluate the capacity of the C-band Synthetic Aperture Radar (SAR) time series imagery, acquired by the European satellite Sentinel-1 (S1), for the agriculture crop classification and its reliability to differentiate onion from sunflower, among others. The work then focused on classifying land cover in intensively cultivated agricultural regions. The study was developed in the Bonaerense Valley of the Colorado River (BVCR), Buenos Aires Province in Argentina, backed up by the field truth of 1634 field samples. In addition to the onion and sunflower crops, there are other crops present in the study area such as cereals, alfalfa, potatoes and maize, which are considered as the image background in the classification process. The field samples database was used for training and supporting a supervised classification with two machine learning algorithms—Random Forest (RF) and Support Vector Machine (SVM)—obtaining high levels of accuracy in each case. Different S1 SAR time-series features were used to assess the performance of S1 crop classification in terms of polarization VH+VV, Grey Level Co-occurrence Matrix (GLCM) image texture and a combination of both. The analysis of SAR data and their features was carried out at OBIA lot level (Object Based Image Analysis) showing an optimal strategy to counteract the effect of the residual and inherent speckle noise of the radar signal. In the process of differentiating onion and sunflower crops, the analysis of the VH+VV stack with the SVM algorithm delivered the best statistical classification results in terms of Overall Accuracy (OA) and Kappa Index, (Kp) when other crops (image background) were not considered (OA = 95.35%, Kp = 0.89). Certainly, the GLCM texture analysis derived from the S1 SAR images is a valuable source of information for obtaining very good classification results. When differentiating sunflower from onion considering also other crops present in the BVCR, the GLCM stack proved to be the most suitable dataset analyzed in this work (OA = 89.98%, Kp = 0.66 for SVM algorithm). This working methodology is applicable to other irrigated valleys in Argentina dedicated to intensive crops. There are also variables inherent to each lot, soil, crop and agricultural producer that differ according to the study area and that should be considered for each case in the future.Fil: Caballero, Gabriel. Universidad Blas Pascal. Centro de Investigación y Desarrollo Aplicado en Informática y Telecomunicaciones (CIADE-IT); ArgentinaFil: Platzech, Gabriel. INVAP. Government & Security Division; ArgentinaFil: Pezzola, Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Casella, Alejandra. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; ArgentinaFil: Winschel, Cristina Ines. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; ArgentinaFil: Silva, Samanta. Ministerio de Desarrollo Agrario (Buenos Aires, provincia). Colorado River Development Corporation (CORFO); ArgentinaFil: Ludueña, Emilia. INGTRADUCCIONES; ArgentinaFil: Pasqualotto, Nieves. Universidad de Valencia. Image Processing Laboratory (IPL); EspañaFil: Delegido, Jesús. Universidad de Valencia. Image Processing Laboratory (IPL); EspañaMDPI2020-10-27T11:11:04Z2020-10-27T11:11:04Z2020-06-13info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfhttp://hdl.handle.net/20.500.12123/8132https://www.mdpi.com/2073-4395/10/6/8452073-4395https://doi.org/10.3390/agronomy10060845Agronomy 10 (6) : 845 (2020)reponame:INTA Digital (INTA)instname:Instituto Nacional de Tecnología Agropecuariaenginfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by-nc-sa/4.0/Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)2026-09-24T11:35:41Zoai:localhost:20.500.12123/8132instacron:INTAInstitucionalhttp://repositorio.inta.gob.ar/Organismo científico-tecnológicoNo correspondehttp://repositorio.inta.gob.ar/oai/requesttripaldi.nicolas@inta.gob.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:l2026-09-24 11:35:43.028INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuariafalse
dc.title.none.fl_str_mv Assessment of multi - date sentinel - 1 polarizations and GLCM texture features capacity for onion and sunflower classification in an irrigated valley: An object level approach
title Assessment of multi - date sentinel - 1 polarizations and GLCM texture features capacity for onion and sunflower classification in an irrigated valley: An object level approach
spellingShingle Assessment of multi - date sentinel - 1 polarizations and GLCM texture features capacity for onion and sunflower classification in an irrigated valley: An object level approach
Caballero, Gabriel
Land Cover
Onions
Cobertura de Suelos
Helianthus annuus
Cebolla
Sentinel 1
Sunflower
Supervised Classification
Centinela 1
Girasol
Clasificación Supervisada
Río Colorado, Buenos Aires
title_short Assessment of multi - date sentinel - 1 polarizations and GLCM texture features capacity for onion and sunflower classification in an irrigated valley: An object level approach
title_full Assessment of multi - date sentinel - 1 polarizations and GLCM texture features capacity for onion and sunflower classification in an irrigated valley: An object level approach
title_fullStr Assessment of multi - date sentinel - 1 polarizations and GLCM texture features capacity for onion and sunflower classification in an irrigated valley: An object level approach
title_full_unstemmed Assessment of multi - date sentinel - 1 polarizations and GLCM texture features capacity for onion and sunflower classification in an irrigated valley: An object level approach
title_sort Assessment of multi - date sentinel - 1 polarizations and GLCM texture features capacity for onion and sunflower classification in an irrigated valley: An object level approach
dc.creator.none.fl_str_mv Caballero, Gabriel
Platzech, Gabriel
Pezzola, Nestor Alejandro
Casella, Alejandra An
Winschel, Cristina Ines
Silva, Samanta
Ludueña, Emilia
Pasqualotto, Nieves
Delegido, Jesús
author Caballero, Gabriel
author_facet Caballero, Gabriel
Platzech, Gabriel
Pezzola, Nestor Alejandro
Casella, Alejandra An
Winschel, Cristina Ines
Silva, Samanta
Ludueña, Emilia
Pasqualotto, Nieves
Delegido, Jesús
author_role author
author2 Platzech, Gabriel
Pezzola, Nestor Alejandro
Casella, Alejandra An
Winschel, Cristina Ines
Silva, Samanta
Ludueña, Emilia
Pasqualotto, Nieves
Delegido, Jesús
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv Land Cover
Onions
Cobertura de Suelos
Helianthus annuus
Cebolla
Sentinel 1
Sunflower
Supervised Classification
Centinela 1
Girasol
Clasificación Supervisada
Río Colorado, Buenos Aires
topic Land Cover
Onions
Cobertura de Suelos
Helianthus annuus
Cebolla
Sentinel 1
Sunflower
Supervised Classification
Centinela 1
Girasol
Clasificación Supervisada
Río Colorado, Buenos Aires
dc.description.none.fl_txt_mv The objective of this work is to evaluate the capacity of the C-band Synthetic Aperture Radar (SAR) time series imagery, acquired by the European satellite Sentinel-1 (S1), for the agriculture crop classification and its reliability to differentiate onion from sunflower, among others. The work then focused on classifying land cover in intensively cultivated agricultural regions. The study was developed in the Bonaerense Valley of the Colorado River (BVCR), Buenos Aires Province in Argentina, backed up by the field truth of 1634 field samples. In addition to the onion and sunflower crops, there are other crops present in the study area such as cereals, alfalfa, potatoes and maize, which are considered as the image background in the classification process. The field samples database was used for training and supporting a supervised classification with two machine learning algorithms—Random Forest (RF) and Support Vector Machine (SVM)—obtaining high levels of accuracy in each case. Different S1 SAR time-series features were used to assess the performance of S1 crop classification in terms of polarization VH+VV, Grey Level Co-occurrence Matrix (GLCM) image texture and a combination of both. The analysis of SAR data and their features was carried out at OBIA lot level (Object Based Image Analysis) showing an optimal strategy to counteract the effect of the residual and inherent speckle noise of the radar signal. In the process of differentiating onion and sunflower crops, the analysis of the VH+VV stack with the SVM algorithm delivered the best statistical classification results in terms of Overall Accuracy (OA) and Kappa Index, (Kp) when other crops (image background) were not considered (OA = 95.35%, Kp = 0.89). Certainly, the GLCM texture analysis derived from the S1 SAR images is a valuable source of information for obtaining very good classification results. When differentiating sunflower from onion considering also other crops present in the BVCR, the GLCM stack proved to be the most suitable dataset analyzed in this work (OA = 89.98%, Kp = 0.66 for SVM algorithm). This working methodology is applicable to other irrigated valleys in Argentina dedicated to intensive crops. There are also variables inherent to each lot, soil, crop and agricultural producer that differ according to the study area and that should be considered for each case in the future.
Fil: Caballero, Gabriel. Universidad Blas Pascal. Centro de Investigación y Desarrollo Aplicado en Informática y Telecomunicaciones (CIADE-IT); Argentina
Fil: Platzech, Gabriel. INVAP. Government & Security Division; Argentina
Fil: Pezzola, Alejandro. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina
Fil: Casella, Alejandra. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina
Fil: Winschel, Cristina Ines. Instituto Nacional de Tecnología Agropecuaria (INTA). Estación Experimental Agropecuaria Hilario Ascasubi; Argentina
Fil: Silva, Samanta. Ministerio de Desarrollo Agrario (Buenos Aires, provincia). Colorado River Development Corporation (CORFO); Argentina
Fil: Ludueña, Emilia. INGTRADUCCIONES; Argentina
Fil: Pasqualotto, Nieves. Universidad de Valencia. Image Processing Laboratory (IPL); España
Fil: Delegido, Jesús. Universidad de Valencia. Image Processing Laboratory (IPL); España
description The objective of this work is to evaluate the capacity of the C-band Synthetic Aperture Radar (SAR) time series imagery, acquired by the European satellite Sentinel-1 (S1), for the agriculture crop classification and its reliability to differentiate onion from sunflower, among others. The work then focused on classifying land cover in intensively cultivated agricultural regions. The study was developed in the Bonaerense Valley of the Colorado River (BVCR), Buenos Aires Province in Argentina, backed up by the field truth of 1634 field samples. In addition to the onion and sunflower crops, there are other crops present in the study area such as cereals, alfalfa, potatoes and maize, which are considered as the image background in the classification process. The field samples database was used for training and supporting a supervised classification with two machine learning algorithms—Random Forest (RF) and Support Vector Machine (SVM)—obtaining high levels of accuracy in each case. Different S1 SAR time-series features were used to assess the performance of S1 crop classification in terms of polarization VH+VV, Grey Level Co-occurrence Matrix (GLCM) image texture and a combination of both. The analysis of SAR data and their features was carried out at OBIA lot level (Object Based Image Analysis) showing an optimal strategy to counteract the effect of the residual and inherent speckle noise of the radar signal. In the process of differentiating onion and sunflower crops, the analysis of the VH+VV stack with the SVM algorithm delivered the best statistical classification results in terms of Overall Accuracy (OA) and Kappa Index, (Kp) when other crops (image background) were not considered (OA = 95.35%, Kp = 0.89). Certainly, the GLCM texture analysis derived from the S1 SAR images is a valuable source of information for obtaining very good classification results. When differentiating sunflower from onion considering also other crops present in the BVCR, the GLCM stack proved to be the most suitable dataset analyzed in this work (OA = 89.98%, Kp = 0.66 for SVM algorithm). This working methodology is applicable to other irrigated valleys in Argentina dedicated to intensive crops. There are also variables inherent to each lot, soil, crop and agricultural producer that differ according to the study area and that should be considered for each case in the future.
publishDate 2020
dc.date.none.fl_str_mv 2020-10-27T11:11:04Z
2020-10-27T11:11:04Z
2020-06-13
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/20.500.12123/8132
https://www.mdpi.com/2073-4395/10/6/845
2073-4395
https://doi.org/10.3390/agronomy10060845
url http://hdl.handle.net/20.500.12123/8132
https://www.mdpi.com/2073-4395/10/6/845
https://doi.org/10.3390/agronomy10060845
identifier_str_mv 2073-4395
dc.language.none.fl_str_mv eng
language eng
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-sa/4.0/
Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
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 Agronomy 10 (6) : 845 (2020)
reponame:INTA Digital (INTA)
instname:Instituto Nacional de Tecnología Agropecuaria
reponame_str INTA Digital (INTA)
collection INTA Digital (INTA)
instname_str Instituto Nacional de Tecnología Agropecuaria
repository.name.fl_str_mv INTA Digital (INTA) - Instituto Nacional de Tecnología Agropecuaria
repository.mail.fl_str_mv tripaldi.nicolas@inta.gob.ar
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