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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume V-2-2020
https://doi.org/10.5194/isprs-annals-V-2-2020-357-2020
© Author(s) 2020. 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-V-2-2020-357-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
03 Aug 2020
| 03 Aug 2020
SELF-SUPERVISED LEARNING FOR MONOCULAR DEPTH ESTIMATION FROM AERIAL IMAGERY
M. Hermann
Fraunhofer IOSB, Karlsruhe, Germany
Institute of Photogrammetry and Remote Sensing, KIT, Karlsruhe, Germany
Fraunhofer Center for Machine Learning
B. Ruf
Fraunhofer IOSB, Karlsruhe, Germany
Institute of Photogrammetry and Remote Sensing, KIT, Karlsruhe, Germany
Fraunhofer Center for Machine Learning
M. Weinmann
Institute of Photogrammetry and Remote Sensing, KIT, Karlsruhe, Germany
S. Hinz
Institute of Photogrammetry and Remote Sensing, KIT, Karlsruhe, Germany
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Short summary
Short summary
This work aims at deriving high spatially resolved maps of atmospheric water vapor by the fusion data from Interferometric Synthetic Aperture Radar (InSAR), Global Navigation Satellite Systems (GNSS), and the Weather Research and Forecasting (WRF) model. The data fusion approach exploits the redundant and complementary spatial properties of all data sets to provide more accurate and high-resolution maps of water vapor. The comparison with maps from MERIS shows rms values of less than 1 mm.
The International Society for Photogrammetry and Remote Sensing is a non-governmental organization devoted to the development of international cooperation for the advancement of photogrammetry and remote sensing and their applications. The Society operates without any discrimination on grounds of race, religion, nationality, or political philosophy.
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