Towards High-Resolution Alignment and Super-Resolution of Multi-Sensor Satellite Imagery
This addresses the challenge of data fusion for geospatial analysis using real sensor data, though it appears incremental as it builds on existing super-resolution techniques.
The paper tackled the problem of fusing multi-sensor satellite imagery with differing resolutions by developing a framework to align and upscale 30m HLS imagery using 10m HLS as a reference, demonstrating effectiveness through evaluations.
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 characteristics. In this work, we develop a preliminary framework to align and upscale Harmonized Landsat Sentinel 30m(HLS 30) imagery using Harmonized Landsat Sentinel 10m(HLS10) as a reference from the HLS dataset. 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.