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
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...
| Main Authors: | , , , , , , , , |
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| Format: | Artículo |
| Language: | Inglés |
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MDPI
2020
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| Online Access: | http://hdl.handle.net/20.500.12123/8132 https://www.mdpi.com/2073-4395/10/6/845 https://doi.org/10.3390/agronomy10060845 |
| _version_ | 1855484139483955200 |
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| author | Caballero, Gabriel Platzech, Gabriel Pezzola, Nestor Alejandro Casella, Alejandra An Winschel, Cristina Ines Silva, Samanta Ludueña, Emilia Pasqualotto, Nieves Delegido, Jesús |
| author_browse | Caballero, Gabriel Casella, Alejandra An Delegido, Jesús Ludueña, Emilia Pasqualotto, Nieves Pezzola, Nestor Alejandro Platzech, Gabriel Silva, Samanta Winschel, Cristina Ines |
| author_facet | Caballero, Gabriel Platzech, Gabriel Pezzola, Nestor Alejandro Casella, Alejandra An Winschel, Cristina Ines Silva, Samanta Ludueña, Emilia Pasqualotto, Nieves Delegido, Jesús |
| author_sort | Caballero, Gabriel |
| collection | INTA Digital |
| 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. |
| format | Artículo |
| id | INTA8132 |
| institution | Instituto Nacional de Tecnología Agropecuaria (INTA -Argentina) |
| language | Inglés |
| publishDate | 2020 |
| publishDateRange | 2020 |
| publishDateSort | 2020 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | INTA81322020-10-27T11:28:12Z 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 Platzech, Gabriel Pezzola, Nestor Alejandro Casella, Alejandra An Winschel, Cristina Ines Silva, Samanta Ludueña, Emilia Pasqualotto, Nieves Delegido, Jesús Land Cover Onions Cobertura de Suelos Helianthus annuus Cebolla Sentinel - 1 Sunflower Supervised Classification Centinela - 1 Girasol Clasificación Supervisada Río Colorado, Buenos Aires 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 2020-10-27T11:11:04Z 2020-10-27T11:11:04Z 2020-06-13 info:ar-repo/semantics/artículo info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion 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 eng 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) application/pdf MDPI Agronomy 10 (6) : 845 (2020) |
| spellingShingle | Land Cover Onions Cobertura de Suelos Helianthus annuus Cebolla Sentinel - 1 Sunflower Supervised Classification Centinela - 1 Girasol Clasificación Supervisada Río Colorado, Buenos Aires Caballero, Gabriel Platzech, Gabriel Pezzola, Nestor Alejandro Casella, Alejandra An Winschel, Cristina Ines Silva, Samanta Ludueña, Emilia Pasqualotto, Nieves Delegido, Jesús 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 |
| 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_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_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 |
| topic | Land Cover Onions Cobertura de Suelos Helianthus annuus Cebolla Sentinel - 1 Sunflower Supervised Classification Centinela - 1 Girasol Clasificación Supervisada Río Colorado, Buenos Aires |
| url | http://hdl.handle.net/20.500.12123/8132 https://www.mdpi.com/2073-4395/10/6/845 https://doi.org/10.3390/agronomy10060845 |
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