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The demand for new tools for mass remote sensing of crops, combined with the open and free availability of satellite imagery, has prompted the development of new methods for crop classification. Because this classification is frequently required to be completed within a specific time frame, performance is also essential. In this work, we propose a new method that creates synthetic images by extracting satellite data at the pixel level, processing all available bands, as well as their data distributed over time considering images from multiple dates. With this approach, data from images of Sentinel-2 are used by a deep convolutional network system, which will extract the necessary information to discern between different types of crops over a year after being trained with data from previous years. Following the proposed methodology, it is possible to classify crops and distinguish between several crop classes while also being computationally low-cost. A software system that implements this method has been used in an area of Extremadura (Spain) as a complementary monitoring tool for the subsidies supported by the Common Agricultural Policy of the European Union.
Guillermo Siesto; Marcos Fernández-Sellers; Adolfo Lozano-Tello. Crop Classification of Satellite Imagery Using Synthetic Multitemporal and Multispectral Images in Convolutional Neural Networks. Remote Sensing 2021, 13, 3378 .
AMA StyleGuillermo Siesto, Marcos Fernández-Sellers, Adolfo Lozano-Tello. Crop Classification of Satellite Imagery Using Synthetic Multitemporal and Multispectral Images in Convolutional Neural Networks. Remote Sensing. 2021; 13 (17):3378.
Chicago/Turabian StyleGuillermo Siesto; Marcos Fernández-Sellers; Adolfo Lozano-Tello. 2021. "Crop Classification of Satellite Imagery Using Synthetic Multitemporal and Multispectral Images in Convolutional Neural Networks." Remote Sensing 13, no. 17: 3378.
The early and automatic identification of crops declared by farmers is essential for streamlining European Union Common Agricultural Policy (CAP) payment processes. Currently, field inspections are partial, expensive and entail a considerable delay in the process. Chronological satellite images of cultivated plots can be used so that neural networks can form the model of the declared crop. Once the patterns of a crop are obtained, the correspondence of the declaration with the model of the neural network can be systematically predicted, and can be used for monitoring the CAP. In this article, we propose a learning model with neural networks, using as examples of training the pixels of the cultivated plots from the satellite images over a period of time. We also propose using several years in the training model to generalise the patterns without linking them to the climatic characteristics of a specific year. The article also describes the use of the model in learning the multi-year pattern of tobacco cultivation with very good results.
Adolfo Lozano-Tello; Marcos Fernández-Sellers; Elia Quirós; Laura Fragoso-Campón; Abelardo García-Martín; José Antonio Gutiérrez Gallego; Carmen Mateos; Rubén Trenado; Pedro Muñoz. Crop identification by massive processing of multiannual satellite imagery for EU common agriculture policy subsidy control. European Journal of Remote Sensing 2020, 54, 1 -12.
AMA StyleAdolfo Lozano-Tello, Marcos Fernández-Sellers, Elia Quirós, Laura Fragoso-Campón, Abelardo García-Martín, José Antonio Gutiérrez Gallego, Carmen Mateos, Rubén Trenado, Pedro Muñoz. Crop identification by massive processing of multiannual satellite imagery for EU common agriculture policy subsidy control. European Journal of Remote Sensing. 2020; 54 (1):1-12.
Chicago/Turabian StyleAdolfo Lozano-Tello; Marcos Fernández-Sellers; Elia Quirós; Laura Fragoso-Campón; Abelardo García-Martín; José Antonio Gutiérrez Gallego; Carmen Mateos; Rubén Trenado; Pedro Muñoz. 2020. "Crop identification by massive processing of multiannual satellite imagery for EU common agriculture policy subsidy control." European Journal of Remote Sensing 54, no. 1: 1-12.
Marcos Fernandez-Sellers; Julio Acedo; Adolfo Lozano-Tello. Identification of representative terms of datasets. 2019 14th Iberian Conference on Information Systems and Technologies (CISTI) 2019, 1 .
AMA StyleMarcos Fernandez-Sellers, Julio Acedo, Adolfo Lozano-Tello. Identification of representative terms of datasets. 2019 14th Iberian Conference on Information Systems and Technologies (CISTI). 2019; ():1.
Chicago/Turabian StyleMarcos Fernandez-Sellers; Julio Acedo; Adolfo Lozano-Tello. 2019. "Identification of representative terms of datasets." 2019 14th Iberian Conference on Information Systems and Technologies (CISTI) , no. : 1.
Julio Acedo; Marcos Fernandez-Sellers; Adolfo Lozano-Tello. Detection model of similar datasets. 2019 14th Iberian Conference on Information Systems and Technologies (CISTI) 2019, 1 .
AMA StyleJulio Acedo, Marcos Fernandez-Sellers, Adolfo Lozano-Tello. Detection model of similar datasets. 2019 14th Iberian Conference on Information Systems and Technologies (CISTI). 2019; ():1.
Chicago/Turabian StyleJulio Acedo; Marcos Fernandez-Sellers; Adolfo Lozano-Tello. 2019. "Detection model of similar datasets." 2019 14th Iberian Conference on Information Systems and Technologies (CISTI) , no. : 1.
Julio Acedo; Adolfo Lozano-Tello; Marcos Fernandez-Sellers. Model of datasets unification from different open data portals. 2018 13th Iberian Conference on Information Systems and Technologies (CISTI) 2018, 1 .
AMA StyleJulio Acedo, Adolfo Lozano-Tello, Marcos Fernandez-Sellers. Model of datasets unification from different open data portals. 2018 13th Iberian Conference on Information Systems and Technologies (CISTI). 2018; ():1.
Chicago/Turabian StyleJulio Acedo; Adolfo Lozano-Tello; Marcos Fernandez-Sellers. 2018. "Model of datasets unification from different open data portals." 2018 13th Iberian Conference on Information Systems and Technologies (CISTI) , no. : 1.