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Authors:
Recla, Michael; Schmitt, Michael 
Document type:
Konferenzbeitrag / Conference Paper 
Title:
Deep Learning-based DSM Generation from Dual-Aspect SAR Data 
Title of conference publication:
ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences 
Subtitle of conference publication:
The Role of Photogrammetry for a Sustainable World 
Series volume:
X-2-2024 
Conference title:
ISPRS Technical Commission II Mid-term Symposium (2024, Las Vegas, Nev.) 
Venue:
Las Vegas, Nevada, USA 
Year of conference:
2024 
Date of conference beginning:
11.06.2024 
Date of conference ending:
14.06.2024 
Year:
2024 
Pages from - to:
193-200 
Language:
Englisch 
Keywords:
Deep Learning ; Synthetic Aperture Radar (SAR) ; 3D Reconstruction ; Radargrammetry ; DSM Generation 
Abstract:
Rapid mapping demands efficient methods for a fast extraction of information from satellite data while minimizing data requirements. This paper explores the potential of deep learning for the generation of high-resolution urban elevation data from Synthetic Aperture Radar (SAR) imagery. In order to mitigate occlusion effects caused by the side-looking nature of SAR remote sensing, two SAR images from opposing aspects are leveraged and processed in an end-to-end deep neural network. The presented...    »
 
Department:
Fakultät für Luft- und Raumfahrttechnik 
Institute:
LRT 9 - Institut für Raumfahrttechnik und Weltraumnutzung 
Chair:
Schmitt, Michael 
Open Access yes or no?:
Ja / Yes 
Type of OA license:
CC BY 4.0