Crop type mapping based on Sentinel-1 backscatter time series
Fecha
2018Versión
Acceso abierto / Sarbide irekia
Tipo
Contribución a congreso / Biltzarrerako ekarpena
Versión
Versión aceptada / Onetsi den bertsioa
Identificador del proyecto
ES/1PE/CGL2016-75217-R
Impacto
|
10.1109/IGARSS.2018.8519005
Resumen
The high revisit time of Sentinel-1 (S1) observations enables the design of crop type mapping approaches exploiting the backscatter time series observed for the different crops. The objective of this study is to propose a supervised crop classification methodology based on the temporal signature of crops. With this aim 29 dual-pol S1 observations acquired over an agricultural area of Spain, where ...
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The high revisit time of Sentinel-1 (S1) observations enables the design of crop type mapping approaches exploiting the backscatter time series observed for the different crops. The objective of this study is to propose a supervised crop classification methodology based on the temporal signature of crops. With this aim 29 dual-pol S1 observations acquired over an agricultural area of Spain, where ground truth was available, were processed. The classification approach was based on the temporal signatures obtained for each polarization channel (VH, VV and the cross-pol ratio) for the different crops. Highest accuracies were obtained when fields were assigned to the class that minimized the RMSE, with an overall accuracy of 79% and best results for rapeseed, sunflower, alfalfa and barley. [--]
Materias
Crop type mapping,
Supervised classification,
Sentinel-1,
Time series
Editor
IEEE
Publicado en
2019 IEEE International Geoscience & Remote Sensing Symposium (IGARSS): proceedings. July 22–27, 2018, Valencia, Spain
Departamento
Universidad Pública de Navarra. Departamento de Ingeniería /
Nafarroako Unibertsitate Publikoa. Ingeniaritza Saila
Versión del editor
Entidades Financiadoras
This work was partly funded by project CGL2016-75217-R (MINECO/FEDER, EU) and project PyrenEOS EFA 048/15, the latter has been 65% cofinanced by the European Regional Development Fund (ERDF) through the Interreg V-A Spain-France Andorra programme POCTEFA 2014-2020).