A Model-Based Deep Transfer Learning Algorithm for Phenology Forecasting Using Satellite Imagery

  1. M.Á. Molina 1
  2. M.J. Jiménez-Navarro 1
  3. F. Martínez- Álvarez 1
  4. G. Asencio-Cortés 1
  1. 1 Universidad Pablo de Olavide
    info

    Universidad Pablo de Olavide

    Sevilla, España

    ROR https://ror.org/02z749649

Libro:
Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021. Bilbao, Spain. September 22–24, 2021. Proceedings
  1. Hugo Sanjurjo González (coord.)
  2. Iker Pastor López (coord.)
  3. Pablo García Bringas (coord.)
  4. Héctor Quintián (coord.)
  5. Emilio Corchado (coord.)

Editorial: Springer International Publishing AG

ISBN: 978-3-030-86271-8,  978-3-030-86270-1

Año de publicación: 2021

Páginas: 511-523

Congreso: Hybrid Artificial Intelligent Systems (HAIS) (16. 2021. Bilbao)

Tipo: Aportación congreso

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Resumen

A new transfer learning strategy is proposed for classification in this work, based on fully connected neural networks. The transfer learning process consists in a training phase of the neural network on a source dataset. Then, the last two layers are retrained using a different small target dataset. Clustering techniques are also applied in order to determine the most suitable data to be used as target. A preliminary study has been conducted to train and test the transfer learning proposal on the classification problem of phenology forecasting, by using up to sixteen different parcels located in Spain. The results achieved are quite promising and encourage conducting further research in this field, having led to a 7.65% of improvement with respect to other three different strategies with both transfer and non-transfer learning models.

Fuente de los datos: Dialnet