Towards High-Resolution Alignment and Super-Resolution of Multi-Sensor Satellite Imagery

Philip Wootaek Shin, Vishal Gaur, Rahul Ramachandran, Manil Maskey, Jack Sampson, Vijaykrishnan Narayanan, Sujit Roy; Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) Workshops, 2025, pp. 2778-2786

High-resolution satellite imagery is essential for geospatial analysis, yet differences in spatial resolution across satellite sensors present challenges for data fusion and downstream applications. Super-resolution techniques can help bridge this gap, but existing methods rely on artificially downscaled images rather than real sensor data and are not well suited for heterogeneous satellite sensors with differing spectral, temporal characteristic. In this work, we develop a preliminary framework to align and upscale Landsat-8/9 imagery (30 meters per pixel) using Sentinel-2 (10 meters per pixel) as a reference. Our approach aims to bridge the resolution gap between these sensors and improve the quality of super-resolved Landsat imagery. Quantitative and qualitative evaluations demonstrate the effectiveness of our method, showing its potential for enhancing satellite-based sensing applications. This study provides insights into the feasibility of heterogeneous satellite image super-resolution and highlights key considerations for future advancements in the field.