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

M.Á. Molina ^[1] ; M.J. Jiménez-Navarro ^[1] ; F. Martínez- Álvarez ^[1] ; G. Asencio-Cortés ^[1]
1. [1] Universidad Pablo de Olavide
  
  Universidad Pablo de Olavide
  
  Sevilla, España
Localización: Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021. Bilbao, Spain. September 22–24, 2021. Proceedings / coord. por Hugo Sanjurjo González, Iker Pastor López , Pablo García Bringas , Héctor Quintián Pardo , Emilio Santiago Corchado Rodríguez , 2021, ISBN 978-3-030-86271-8, págs. 511-523
Idioma: inglés
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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.