Deep learning para la clasificación de usos de suelo agrícola con Sentinel-2

  1. Campos-Taberner, M. 1
  2. García-Haro, F.J. 1
  3. Martínez, B. 1
  4. Gilabert, M.A. 1
  1. 1 Universitat de València
    info

    Universitat de València

    Valencia, España

    ROR https://ror.org/043nxc105

Revista:
Revista de teledetección: Revista de la Asociación Española de Teledetección

ISSN: 1133-0953

Año de publicación: 2020

Título del ejemplar: Applications of Copernicus Sentinel Satellites; V-XI

Número: 56

Páginas: 35-48

Tipo: Artículo

DOI: 10.4995/RAET.2020.13337 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: Revista de teledetección: Revista de la Asociación Española de Teledetección

Objetivos de desarrollo sostenible

Resumen

The use of deep learning techniques for remote sensing applications has recently increased. These algorithms have proven to be successful in estimation of parameters and classification of images. However, little effort has been made to make them understandable, leading to their implementation as “black boxes”. This work aims to evaluate the performance and clarify the operation of a deep learning algorithm, based on a bi-directional recurrent network of long short-term memory (2-BiLSTM). The land use classification in the Valencian Community based on Sentinel-2 image time series in the framework of the common agricultural policy (CAP) is used as an example. It is verified that the accuracy of the deep learning techniques is superior (98.6 % overall success) to that other algorithms such as decision trees (DT), k-nearest neighbors (k-NN), neural networks (NN), support vector machines (SVM) and random forests (RF). The performance of the classifier has been studied as a function of time and of the predictors used. It is concluded that, in the study area, the most relevant information used by the network in the classification are the images corresponding to summer and the spectral and spatial information derived from the red and near infrared bands. These results open the door to new studies in the field of the explainable deep learning in remote sensing applications.

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Información de financiación

Este trabajo ha sido subvencionado gracias al Convenio 2019 y 2020 de colaboraci?n entre la Generalitat Valenciana, a trav?s de la Conselleria d?Agricultura, Medi Ambient, Canvi Clim?tic i Desenvolupament Rural, y la Universitat de Val?ncia ? Estudi General.

Financiadores

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Fuente de los datos: Dialnet