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

TL;DR

This paper proposes a framework for aligning and super-resolving multi-sensor satellite imagery, specifically enhancing Landsat images by leveraging higher-resolution Sentinel data, addressing heterogeneity challenges.

Contribution

It introduces a novel approach to align and upscale heterogeneous satellite images, bridging resolution gaps using real sensor data rather than synthetic downscaling.

Findings

Effective super-resolution of Landsat imagery demonstrated

Method improves alignment between heterogeneous sensors

Potential to enhance satellite data applications

Abstract

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.