Depth Separable architecture for Sentinel-5P Super-Resolution
Hyam Omar Ali, Romain Abraham, Bruno Galerne

TL;DR
This paper introduces a novel depth separable convolution-based super-resolution model tailored for Sentinel-5P satellite data, significantly enhancing spatial resolution and detail capture in hyperspectral atmospheric measurements.
Contribution
The work presents a new super-resolution model using Depth Separable Convolution architecture specifically designed for Sentinel-5P hyperspectral data, outperforming existing methods.
Findings
Model outperforms existing super-resolution techniques on most spectral bands.
Effective exploitation of cross-channel correlations improves spatial detail.
Enhances the potential for precise atmospheric analysis and remote sensing applications.
Abstract
Sentinel-5P (S5P) satellite provides atmospheric measurements for air quality and climate monitoring. While the S5P satellite offers rich spectral resolution, it inherits physical limitations that restricts its spatial resolution. Super-resolution (SR) techniques can overcome these limitations and enhance the spatial resolution of S5P data. In this work, we introduce a novel SR model specifically designed for S5P data that have eight spectral bands with around 500 channels for each band. Our proposed S5-DSCR model relies on Depth Separable Convolution (DSC) architecture to effectively perform spatial SR by exploiting cross-channel correlations. Quantitative evaluation demonstrates that our model outperforms existing methods for the majority of the spectral bands. This work highlights the potential of leveraging DSC architecture to address the challenges of hyperspectral SR. Our model…
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Taxonomy
TopicsPhotoacoustic and Ultrasonic Imaging · Advanced Image Fusion Techniques · Meteorological Phenomena and Simulations
MethodsConvolution
