Distilling Before Refine: Spatio-Temporal Transfer Learning for Mapping Irrigated Areas Using Sentinel-1 Time Series

Abstract

This letter proposes a deep learning model to deal with the spatial transfer challenge for the mapping of irrigated areas through the analysis of Sentinel-1 data. First, a convolutional neural network (CNN) model called “Teacher Model” is trained on a source geographical area characterized by a huge volume of samples. Then, this model is transferred from the source area to the target area characterized by a limited number of samples. The transfer learning framework is based on a distill and refine strategy, in which the teacher model is first distilled into a student model and, successively, refined by data samples coming from the target geographical area. The proposed strategy is compared with different approaches including a random forest (RF) classifier trained on the target data set and a CNN trained on the source data set and directly applied on the target area as well as several CNN classifiers trained on the target data set. The evaluation of the performed transfer strategy shows that the “distill and refine” framework obtains the best performance compared with other competing approaches. The obtained findings represent a first step toward the understanding of the spatial transferability of deep learning models in the Earth observation domain.