Self-Supervised Vision Transformers for Land-Cover Segmentation and Classification
Transformer models have recently approached or even surpassed the performance of ConvNets on computer vision tasks like classification and segmentation. To a large degree, these successes have been enabled by the use of large-scale labelled image datasets for supervised pre-training. This poses a significant challenge for the adaption of vision Transformers to domains where datasets with millions of labelled samples are not available. In this work, we bridge the gap between ConvNets and Transformers for Earth observation by self-supervised pre-training on large-scale unlabelled remote sensing data. We show that self-supervised pre-training yields latent task-agnostic representations that can be utilized for both land cover classification and segmentation tasks, where they significantly outperform the fully supervised baselines. Additionally, we find that subsequent fine-tuning of Transformers for specific downstream tasks performs on-par with commonly used ConvNet architectures. An ablation study further illustrates that the labelled dataset size can be reduced to one-tenth after self-supervised pre-training while still maintaining the performance of the fully supervised approach.