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

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.