Exploring PolSAR Images Representation via Self-Supervised Learning and Its Application on Few-Shot Classification

Abstract

Deep learning methods have attracted much attention in the field of polarimetric synthetic aperture radar (PolSAR) image classification over the past few years. However, for supervised learning based methods, it is quite difficult to get large amounts of high-quality and labeled PolSAR data in real applications. In addition, there is a problem of poor generalization for the method of specific supervision labels. To solve the above issue, we explore how to learn representations from unlabeled data from a new perspective. In this letter, a self-supervised PolSAR representation learning (SSPRL) method is proposed. Different from supervised learning based methods, SSPRL aims to learn PolSAR image representations via unsupervised learning approach. Specifically, a self-supervised learning (SSL) method without negative samples is explored and a positive sample generation approach and a novel encoder architecture designed for PolSAR images are proposed. Moreover, mixup is implemented as a regularization strategy. Further the convolutional encoder is utilized to transfer the feature representation from the unlabeled PolSAR data to the downstream task, that is, to achieve the few-shot PolSAR classification. Comparative experimental results on two widely-used PolSAR benchmark datasets verify the effectiveness of proposed method and demonstrate that SSPRL produces impressive performance on few-shot classification task compared with state-of-the-art algorithms.