Deep Self-Supervised Disturbance Mapping with the OPERA Sentinel-1 Radiometric Terrain Corrected SAR Backscatter Product

TL;DR

This paper introduces a self-supervised vision transformer approach using the OPERA RTC-S1 SAR dataset to effectively map land surface disturbances globally without requiring labeled data, outperforming existing methods.

Contribution

It presents a novel self-supervised vision transformer model trained on analysis-ready SAR data for disturbance detection, eliminating the need for labeled training data.

Findings

High-quality disturbance delineation with F1 scores > 0.6

Outperforms existing SAR disturbance detection methods

Effective across different natural disaster types and regions

Abstract

Mapping land surface disturbances supports disaster response, resource and ecosystem management, and climate adaptation efforts. Synthetic aperture radar (SAR) is an invaluable tool for disturbance mapping, providing consistent time-series images of the ground regardless of weather or illumination conditions. Despite SAR's potential for disturbance mapping, processing SAR data to an analysis-ready format requires expertise and significant compute resources, particularly for large-scale global analysis. In October 2023, NASA's Observational Products for End-Users from Remote Sensing Analysis (OPERA) project released the near-global Radiometric Terrain Corrected SAR backscatter from Sentinel-1 (RTC-S1) dataset, providing publicly available, analysis-ready SAR imagery. In this work, we utilize this new dataset to systematically analyze land surface disturbances. As labeling SAR data is often prohibitively time-consuming, we train a self-supervised vision transformer - which requires no labels to train - on OPERA RTC-S1 data to estimate a per-pixel distribution from the set of baseline imagery and assess disturbances when there is significant deviation from the modeled distribution. To test our model's capability and generality, we evaluate three different natural disasters - which represent high-intensity, abrupt disturbances - from three different regions of the world. Across events, our approach yields high quality delineations: F1 scores exceeding 0.6 and Areas Under the Precision-Recall Curve exceeding 0.65, consistently outperforming existing SAR disturbance methods. Our findings suggest that a self-supervised vision transformer is well-suited for global disturbance mapping and can be a valuable tool for operational, near-global disturbance monitoring, particularly when labeled data does not exist.