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001047697 1001_ $$00000-0002-9091-3684$$aMajor, David$$b0$$eCorresponding author
001047697 245__ $$aA holistic approach for multi-spectral Sentinel-2 super-resolution and spectral evaluation
001047697 260__ $$aLondon$$bTaylor & Francis$$c2025
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001047697 520__ $$aImages provided by the European Copernicus Sentinel-2 satellites are valuable and easily accessible sources of remote sensing data for tasks across various fields. These data have a high spectral and temporal resolution, but a rather low spatial resolution, limiting their applicability for many tasks. In agricultural tasks, such as crop monitoring of small land parcels, the use of these data for fine-scale analysis is contingent upon the enhancement of spatial resolution while maintaining spectral fidelity. In this work, we propose a comprehensive single-image super-resolution reconstruction workflow that ensures both properties and is divided into two parts. First, a deep learning-based super-resolution reconstruction approach is applied to improve the spatial resolution of multi-spectral Sentinel-2 images to 2.5 m. For this purpose, a novel method is applied to achieve super-resolution of multiple spectral bands where associated real-word reference data is only partially available. It learns to increase the spatial resolution while preserving spectral accuracy of 10 m bands using high-resolution data from an auxiliary satellite with spectral correspondence, and 20 m bands without reference data using synthetic Sentinel-2 pairs. Second, the suitability of the method to subsequent agricultural tasks is evaluated by measuring the discrepancy between the super-resolved and reference data through a novel spectral knowledge-based validation method. This method leverages mappings of reflectances to spectral categories that enable assessing the spectral fidelity of super-resolved outputs, which is complementary to existing image quality assessment metrics, but with greater depth. The promising spectral validation results suggest that our super-resolution reconstruction pipeline has a great potential for agricultural applications.
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001047697 7001_ $$00000-0001-7188-6564$$aHorváth, Zsolt$$b1
001047697 7001_ $$0P:(DE-Juel1)206952$$aKröber, Felix$$b2$$ufzj
001047697 7001_ $$00000-0002-3334-5350$$aAugustin, Hannah$$b3
001047697 7001_ $$00000-0002-0473-1260$$aSudmanns, Martin$$b4
001047697 7001_ $$00009-0005-1564-8525$$aŠevčík, Petr$$b5
001047697 7001_ $$00000-0001-5196-9944$$aBaraldi, Andrea$$b6
001047697 7001_ $$00000-0002-2300-2661$$aBerg, Astrid$$b7
001047697 7001_ $$00000-0002-2481-6720$$aCornel, Daniel$$b8
001047697 7001_ $$00000-0002-5473-3344$$aTiede, Dirk$$b9
001047697 773__ $$0PERI:(DE-600)1497529-4$$a10.1080/01431161.2025.2549132$$gVol. 46, no. 20, p. 7437 - 7464$$n20$$p7437 - 7464$$tInternational journal of remote sensing$$v46$$x0143-1161$$y2025
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