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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XI-2-2026
https://doi.org/10.5194/isprs-annals-XI-2-2026-493-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/isprs-annals-XI-2-2026-493-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
03 Jul 2026
| 03 Jul 2026
From Aerial to Satellite: Can Super-Resolution Enable Label-Free Model Transfer?
Nina Merkle
Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
Corentin Henry
Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
Sandeep Kumar Jangir
Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
Jens Hellekes
Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
Felix Rauch
Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
Pablo d'Angelo
Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
Franz Kurz
Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
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Jingliang Hu, Rong Liu, Danfeng Hong, Andrés Camero, Jing Yao, Mathias Schneider, Franz Kurz, Karl Segl, and Xiao Xiang Zhu
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Multimodal data fusion is an intuitive strategy to break the limitation of individual data in Earth observation. Here, we present a multimodal data set, named MDAS, consisting of synthetic aperture radar (SAR), multispectral, hyperspectral, digital surface model (DSM), and geographic information system (GIS) data for the city of Augsburg, Germany, along with baseline models for resolution enhancement, spectral unmixing, and land cover classification, three typical remote sensing applications.
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J. Tian, X. Zhuo, X. Yuan, C. Henry, P. d’Angelo, and T. Krauss
ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-1-2022, 145–152, https://doi.org/10.5194/isprs-annals-V-1-2022-145-2022, https://doi.org/10.5194/isprs-annals-V-1-2022-145-2022, 2022
T. Krauß, F. Kurz, and H. Runge
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