Self-Supervised Remote Sensing Image Retrieval

Abstract

Current remote sensing platforms generate a vast amount of imagery but the best current methods to index and retrieve that data require expensive and difficult to procure labels. In this paper, we aim to address this problem by presenting a performant content based image retrieval (CBIR) system that is capable of indexing and retrieval using only unlabelled data. We investigate the use of self-supervised learning, a method for end-to-end learning of visual features from large datasets. In particular, we investigate the performance of four state-of-the-art self-supervised learning methods: variational autoencoders, bidirectional GANs, colourisation networks and DeepCluster, and evaluate the quality of the representations learned on remote sensing CBIR problems. Experiments on two very high resolution datasets show that the best of these methods, DeepCluster, is able to achieve near parity with supervised transfer learning despite not using any label information.