Beyond Pretty Pictures: Combined Single- and Multi-Image Super-resolution for Sentinel-2 Images
Aditya Retnanto (1), Son Le (1), Sebastian Mueller (1), Armin Leitner (2), Michael Riffler (2), Konrad Schindler (3), Yohan Iddawela (1) ((1) Asian Development Bank, Philippines, (2) GeoVille Information Systems, Data Processing GmbH, Austria, (3) ETH Z\"urich, Switzerland)

TL;DR
This paper introduces SEN4X, a hybrid super-resolution method that combines single- and multi-image techniques with learned priors to enhance Sentinel-2 satellite images to 2.5 m resolution, improving urban land-cover classification.
Contribution
The paper presents SEN4X, a novel hybrid super-resolution architecture that integrates temporal oversampling and learned priors from high-resolution data for Sentinel-2 images.
Findings
Significant improvement in urban land-cover classification accuracy.
Outperforms state-of-the-art super-resolution baselines.
Enhances Sentinel-2 resolution to 2.5 meters.
Abstract
Super-resolution aims to increase the resolution of satellite images by reconstructing high-frequency details, which go beyond na\"ive upsampling. This has particular relevance for Earth observation missions like Sentinel-2, which offer frequent, regular coverage at no cost; but at coarse resolution. Its pixel footprint is too large to capture small features like houses, streets, or hedge rows. To address this, we present SEN4X, a hybrid super-resolution architecture that combines the advantages of single-image and multi-image techniques. It combines temporal oversampling from repeated Sentinel-2 acquisitions with a learned prior from high-resolution Pl\'eiades Neo data. In doing so, SEN4X upgrades Sentinel-2 imagery to 2.5 m ground sampling distance. We test the super-resolved images on urban land-cover classification in Hanoi, Vietnam. We find that they lead to a significant…
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Taxonomy
TopicsAdvanced Image Processing Techniques · Photoacoustic and Ultrasonic Imaging · Image Processing Techniques and Applications
