UOrtos: Methodology for Co-Registration and Subpixel Georeferencing of Satellite Imagery for Coastal Monitoring

This study introduces a novel methodology for the automated co-registration and georeferencing of satellite imagery to enhance the accuracy of shoreline detection and coastal monitoring. The approach utilizes feature-based methods, cross-correlation, and RANSAC (RANdom SAmple Consensus) algorithms to accurately align images while avoiding outliers. By collectively analyzing the entire set of images and clustering them based on their pixel-pair connections, the method ensures robust transformations across the dataset. The methodology is applied to Sentinel-2 and Landsat images across four coastal sites (Duck, Narrabeen, Torrey Pines, and Truc Vert) from January 2020 to December 2023. The results show that the proposed approach effectively reduces the errors from ∼1 to at least 0.4 px (although they are likely below 0.2 px). This approach can enhance the precision of existing algorithms for coastal feature tracking, such as shoreline detection, and aids in differentiating georeferencing errors from the actual impacts of storms or beach nourishment activities. The tool can also handle complex cases of significant image rotation due to varied projections. The findings emphasize the importance of co-registration for reliable shoreline monitoring, with potential applications in coastal management and climate change impact studies.