- Location
-
Deutsche Nationalbibliothek Frankfurt am Main
- Extent
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Online-Ressource
- Language
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Englisch
- Bibliographic citation
-
FLOOD DETECTION IN TIME SERIES OF OPTICAL AND SAR IMAGES ; volume:XLIII-B2-2020 ; year:2020 ; pages:1343-1346
The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ; XLIII-B2-2020 (2020), 1343-1346
- Creator
-
Rambour, C.
Audebert, N.
Koeniguer, E.
Le Saux, B.
Crucianu, M.
Datcu, M.
- DOI
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10.5194/isprs-archives-XLIII-B2-2020-1343-2020
- URN
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urn:nbn:de:101:1-2020082005272526169142
- Rights
-
Open Access; Der Zugriff auf das Objekt ist unbeschränkt möglich.
- Last update
-
14.08.2025, 11:02 AM CEST
Data provider
This object is provided by:
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Deutsche Nationalbibliothek. If you have any questions about the object, please contact the data provider.
Associated
- Rambour, C.
- Audebert, N.
- Koeniguer, E.
- Le Saux, B.
- Crucianu, M.
- Datcu, M.
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COMPARISON OF CONVOLUTIONAL NEURAL NETWORKS FOR CLOUDY OPTICAL IMAGES RECONSTRUCTION FROM SINGLE OR MULTITEMPORAL JOINT SAR AND OPTICAL IMAGES
Flood depth estimation by means of high-resolution SAR images and LiDAR data
SAR-SHARPENING IN THE KENNAUGH FRAMEWORK APPLIED TO THE FUSION OF MULTI-MODAL SAR AND OPTICAL IMAGES
ADVANCED CLASSIFICATION OF OPTICAL AND SAR IMAGES FOR URBAN LAND COVER MAPPING
REGISTRATION OF OPTICAL AND SAR SATELLITE IMAGES BASED ON GEOMETRIC FEATURE TEMPLATES
INVESTIGATION OF JOINT VISIBILITY BETWEEN SAR AND OPTICAL IMAGES OF URBAN ENVIRONMENTS
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Deep Convolutional Network Based on Attention Mechanism for Matching Optical and SAR Images
Bayesian information extraction from SAR images
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Flood depth estimation by means of high-resolution SAR images and lidar data
An improved SIFT algorithm for registration between SAR and optical images
COMPARISON OF CONVOLUTIONAL NEURAL NETWORKS FOR CLOUDY OPTICAL IMAGES RECONSTRUCTION FROM SINGLE OR MULTITEMPORAL JOINT SAR AND OPTICAL IMAGES
Flood depth estimation by means of high-resolution SAR images and LiDAR data
SAR-SHARPENING IN THE KENNAUGH FRAMEWORK APPLIED TO THE FUSION OF MULTI-MODAL SAR AND OPTICAL IMAGES
ADVANCED CLASSIFICATION OF OPTICAL AND SAR IMAGES FOR URBAN LAND COVER MAPPING
REGISTRATION OF OPTICAL AND SAR SATELLITE IMAGES BASED ON GEOMETRIC FEATURE TEMPLATES
INVESTIGATION OF JOINT VISIBILITY BETWEEN SAR AND OPTICAL IMAGES OF URBAN ENVIRONMENTS
Flood Mapping Using InSAR Coherence Map
Deep Convolutional Network Based on Attention Mechanism for Matching Optical and SAR Images
Bayesian information extraction from SAR images
Global flood extent segmentation in optical satellite images
Flood depth estimation by means of high-resolution SAR images and lidar data
An improved SIFT algorithm for registration between SAR and optical images
COMPARISON OF CONVOLUTIONAL NEURAL NETWORKS FOR CLOUDY OPTICAL IMAGES RECONSTRUCTION FROM SINGLE OR MULTITEMPORAL JOINT SAR AND OPTICAL IMAGES
Flood depth estimation by means of high-resolution SAR images and LiDAR data
SAR-SHARPENING IN THE KENNAUGH FRAMEWORK APPLIED TO THE FUSION OF MULTI-MODAL SAR AND OPTICAL IMAGES
ADVANCED CLASSIFICATION OF OPTICAL AND SAR IMAGES FOR URBAN LAND COVER MAPPING
REGISTRATION OF OPTICAL AND SAR SATELLITE IMAGES BASED ON GEOMETRIC FEATURE TEMPLATES
INVESTIGATION OF JOINT VISIBILITY BETWEEN SAR AND OPTICAL IMAGES OF URBAN ENVIRONMENTS
Flood Mapping Using InSAR Coherence Map
Deep Convolutional Network Based on Attention Mechanism for Matching Optical and SAR Images