Improving Generalization for Few-Shot Remote Sensing Classification with Meta-Learning

In Remote Sensing (RS) classification, generalization ability is one of the measure that characterizes the success of Machine Learning (ML) models, but is often impeded by the scarse availability of annotated training data. Annotated RS samples are expensive to obtain and can present large disparities when produced by different annotators. In this paper, we utilize Few-Shot Learning (FSL) with meta-learning to address the challenge of generalization using limited amount of training information. The data used in this paper is leveraged from different datasets that have diverse distributions, that means distinct feature spaces. We tested our approach on publicly available RS benchmark datasets to perform few-shot RS image classification using meta-learning. The results of the experiments suggest that our approach is able to generalize well on the unseen data even with limited number of training samples and reasonable training time.