Enhanced Remote Sensing Model Performance Through Self-Supervised Learning with Multi-Spectral Data
Recently, self-supervised learning methods have shown remarkable performance rivaling supervised approaches, particularly in the realm of computer vision. This paper addresses a gap in current literature by focusing on the application of the SwAV (Swapping Assignments between Views) model to pre-train on an extensive dataset comprising one million unlabeled multi-spectral images from Sentinel-2 and Sentinel-1 (including SAR images), we investigate the impact of SSL techniques on multispectral and SAR data as compared to RGB data using crop delineation and land cover classification downstream tasks. Our results demonstrate superior performance exhibited by the 12-channel SwAV pre-trained model compared to RGB-only encodings, underscoring the benefits of SSL for enhancing computer vision for remote sensing applications. Additionally, our findings showcase the potential of SSL for smaller datasets and downstream applications.
