Self-supervised representation learning for SAR change detection

Recent growth in the tasking and collection of Synthetic Aperture Radar (SAR) imagery, in particular commercial satellite availability, provides new opportunities for wide area change monitoring. Classic applications of change detection in SAR compare individual pixels, but as higher resolution imagery has become widely available there is an opportunity to leverage structural image content to produce more informative change identification. Deep learning techniques encompass the state of the art in identifying structure in imagery, but are notoriously data hungry. A recent body of research has grown around a technique called self-supervised representation learning (SSRL) to help minimize the need for handmade labels. We build on this research and train a model for use in SAR change detection. We leverage a SSRL approach known as contrastive learning, which encourages a deep learning model to identify salient image features through noise and other augmentations without an immediate need for hand engineered labels. The representation learned through this process can then be applied to other supervised, or unsupervised tasks, and we demonstrate the use of this learned embedding to identify change across SAR image pairs.