TU2.R9.2
DEGRADATION MODELLING FOR SAR-SUPERRESOLUTION TRAINING DATA
Maria Paula Bustos Moreno, Francescopaolo Sica, Michael Schmitt, University of the Bundeswehr Munich, Germany
Session:
TU2.R9: Resolution Enhancement across sensing dimensions: Spatial, Spectral, Temporal, and Radiometric Oral
Track:
Community Contributed Themes
Location:
TBD
Presentation Time:
Tue, 11 Aug, 11:15 - 11:30
Presentation
Discussion
Resources
No resources available.
Session TU2.R9
TU2.R9.1: A SEMANTICS-ASSISTED DEM SUPER-RESOLUTION FRAMEWORK WITH ADAPTIVE HEIGHT BINNING
Yusheng Ding, Diling Liao, Cuilin Yu, Zhiming Liu, Junyi Liu, Xiuwen Jiang, Liang Zhou, Qingsong Wang, Sun Yat-sen University, China
TU2.R9.2: DEGRADATION MODELLING FOR SAR-SUPERRESOLUTION TRAINING DATA
Maria Paula Bustos Moreno, Francescopaolo Sica, Michael Schmitt, University of the Bundeswehr Munich, Germany
TU2.R9.3: A Variational Autoencoder for Semantic Segmentation of Bimodal Image Time Series
Luca Bergamasco, Francesca Bovolo, Fondazione Bruno Kessler, Italy
TU2.R9.4: Enhancing Spatial Resolution of NDVI Maps via Cross-Modality Hierarchical Data Fusion of Multispectral and High-Resolution RGB Imagery
Daniel Portnoy, Fadi Kizel, Technion-Israel Institute of Technology, Israel
TU2.R9.5: DUAL-ADMM UNFOLDING SPECTRAL SUPER-RESOLUTION WITH DEEP SPECTRAL BAND INTERLEAVING STRATEGY
Jian-Kai Huang, Chia-Hsiang Lin, National Cheng Kung University, Taiwan
Contacts