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ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume V-3-2020
https://doi.org/10.5194/isprs-annals-V-3-2020-795-2020
© 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-3-2020-795-2020
© 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
WEAKLY SUPERVISED SEMANTIC SEGMENTATION OF SATELLITE IMAGES FOR LAND COVER MAPPING – CHALLENGES AND OPPORTUNITIES
M. Schmitt
Signal Processing in Earth Observation, Technical University of Munich, Munich, Germany
J. Prexl
Signal Processing in Earth Observation, Technical University of Munich, Munich, Germany
P. Ebel
Signal Processing in Earth Observation, Technical University of Munich, Munich, Germany
L. Liebel
Chair of Remote Sensing Technology, Technical University of Munich, Munich, Germany
X. X. Zhu
Signal Processing in Earth Observation, Technical University of Munich, Munich, Germany
Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
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ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-2-2020, 533–540, https://doi.org/10.5194/isprs-annals-V-2-2020-533-2020, https://doi.org/10.5194/isprs-annals-V-2-2020-533-2020, 2020
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ISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci., V-3-2020, 787–794, https://doi.org/10.5194/isprs-annals-V-3-2020-787-2020, https://doi.org/10.5194/isprs-annals-V-3-2020-787-2020, 2020
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