SRL-ProtoNet: Self-supervised representation learning for few-shot remote sensing scene classification

Abstract

Using a deep learning method to classify a large amount of labelled remote sensing scene data produces good performance. However, it is challenging for deep learning based methods to generalise to classification tasks with limited data. Few-shot learning allows neural networks to classify unseen categories when confronted with a handful of labelled data. Currently, episodic tasks based on meta-learning can effectively complete few-shot classification, and training an encoder that can conduct representation learning has become an important component of few-shot learning. An end-to-end few-shot remote sensing scene classification model based on ProtoNet and self-supervised learning is proposed. The authors design the Pre-prototype for a more discrete feature space and better integration with self-supervised learning, and also propose the ProtoMixer for higher quality prototypes with a global receptive field. The authors’ method outperforms the existing state-of-the-art self-supervised based methods on three widely used benchmark datasets: UC-Merced, NWPU-RESISC45, and AID. Compare with previous state-of-the-art performance. For the one-shot setting, this method improves by 1.21%, 2.36%, and 0.84% in AID, UC-Merced, and NWPU-RESISC45, respectively. For the five-shot setting, this method surpasses by 0.85%, 2.79%, and 0.74% in the AID, UC-Merced, and NWPU-RESISC45, respectively.