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The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
Articles | Volume XLIII-B2-2021
https://doi.org/10.5194/isprs-archives-XLIII-B2-2021-433-2021
© Author(s) 2021. 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-archives-XLIII-B2-2021-433-2021
© Author(s) 2021. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
28 Jun 2021
| 28 Jun 2021
AUTOMATIC OBJECT SEGMENTATION TO SUPPORT CRISIS MANAGEMENT OF LARGE-SCALE EVENTS
S. M. Azimi
Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
R. Kiefl
German Remote Sensing Data Center, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
V. Gstaiger
Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
R. Bahmanyar
Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
N. Merkle
Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
C. Henry
Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
D. Rosenbaum
Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
F. Kurz
Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, Germany
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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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