Video Based Cross-View Geo-Localization of UAV Imagery

This master’s thesis investigates the potential of enhancing cross-view localization (CVL) of UAV imagery using video input. The study, conducted in collaboration with Maxar Intelligence and Linköping University, aims to improve localization performance by leveraging sequential image data captured by UAVs and with satellite imagery as a reference. The thesis explores three main questions; the potential of modifying single-image CVL models to process image sequences, the adaptability of existing ground-to-satellite video CVL models for UAV-to-satellite localization, and the performance comparison of aerial views and satellite imagery extracted from Maxar’s 3D reconstructed models with public datasets. To address these questions, synthetic datasets mimicking University-1652 and SUES-200 were created using Maxar’s 3D data. The results demonstrated that while single-image models like Sample4Geo achieved high accuracy on synthetic datasets, their performance diminished with data that more closely represented the real world due to overfitting to the uniformity of synthetic environments. Existing ground-to-satellite video CVL models faced practical challenges when adapted to UAV perspectives, highlighting the need for tailored approaches. The synthetic datasets from Maxar outperformed public datasets but lacked the variability and realism of real-world conditions. Furthermore, the study introduced a novel multiple-image CVL model that leverages a post-processing technique using density-based clustering (OPTICS) to enhance localization accuracy by filtering noisy predictions and identifying high confidence clusters. The findings indicate that while this approach improves localization performance, significant advancements are required to fully utilize video input for UAV-to-satellite localization in dynamic environments.