With the creation of large-scale annotated datasets such as the ImageNet, fully-supervised machine learning methods have become the standard for solving computer vision tasks. These methods require large amounts of labeled data, which are usually obtained with crowdsourcing tools or social media tags. However, these approaches do not scale for specialized domains, such as medical or satellite imaging, where annotations must be provided by experts at a prohibitive cost. Recently, self-supervised learning has emerged as an alternative for obtaining transferable visual representations from unlabeled data. Models based on these representations match the performance of fully-supervised models while only requiring a small fraction of the annotations. In this work, we aim to explore the application of self-supervised learning methods in the remote sensing domain. We propose a contrastive approach for learning visual representations by exploiting the multi-spectral information of satellite images. These representations serve as a good starting point for a variety of downstream tasks that involve remote sensing imagery, accelerating convergence with fewer labeled examples.

Best thesis award 2020 (draw with 4 more other works)