Resumen de Método de identificación de cultivos mediante el análisis masivo multitemporal de imágenes multiespectrales de satélite

Marcos Fernández Sellers

• -Motivación- La detección de cultivos mediante imágenes de satélite ha experimentado un gran auge en los últimos años, debido principalmente a dos factores: por un lado, las nuevas misiones satelitales; y, por otro lado, la popularización de sistemas de aprendizaje máquina aplicados a la monitorización de cultivos. En trabajos relacionados se pueden apreciar propuestas referidas a la detección de cultivos, pero que se centran en cultivos, regiones o técnicas concretas. La literatura carece de una metodología completa que permita implementar un sistema de identificación de cultivos desde cero, y que pueda adaptarse de manera concreta a las necesidades de cada proyecto.

  -Propuesta- Esta tesis doctoral propone un método completo de identificación de cultivos, haciendo uso de imágenes de satélite multiespectrales, y realizando el análisis de las imágenes a lo largo del tiempo. El método comprende una serie de fases y recomendaciones. El método propone cómo seleccionar y estandarizar la información de los recintos agrícolas que se desea analizar. Posteriormente, tiene lugar la descarga de imágenes, y el etiquetado, selección y filtrado de datos. Desde estos datos, el método propone la generación de imágenes sintéticas multitemporales y multiespectrales para el aprendizaje, y todas las cuestiones relacionadas con el diseño del sistema. Finalmente, la propuesta incluye un método específico para determinar el periodo de detección adecuado de los cultivos. El resultado de aplicar el método consiste en un sistema software completo para la identificación de cultivos. Este trabajo incluye también un caso de uso para probar su aplicabilidad a un proyecto real.

  -Conclusiones- La principal contribución de este trabajo es un método que describe los procedimientos y técnicas para desarrollar un sistema de análisis de cultivos usando imágenes satelitales. El ámbito de aplicación es óptimo para grandes extensiones de terreno, y con un número elevado de recintos, por lo que se denomina de tratamiento masivo. Además, se basa en el análisis múltiple de imágenes de cada recinto para considerar su evolución en el tiempo, por lo que también se le denomina multitemporal.

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