WE2.R13.1: ASSESSMENT OF L-BAND BISTATIC CORRELATION TOMOGRAPHY FOR FORESTRY APPLICATIONS: THEORETICAL MODELS AND EXPERIMENTAL RESULTS
Francesco Salvaterra, Politecnico di Milano, Italy; Francesco Banda, Aresys, Italy; Stefano Tebaldini, Politecnico di Milano, Italy; Mauro Mariotti D'Alessandro, Synspective, Japan
WE2.R13.2: A DEEP LEARNING SOLUTION TO PHASE CALIBRATION OF SAR TOMOGRAPHY
Hossein Aghababaei, University of Twente, Netherlands; Sergio Vitale, Università degli Studi di Napoli Parthenope, Napoli, Italy., Italy; Giampaolo Ferraioli, 2Università degli Studi di Napoli Parthenope, Napoli, Italy, Italy
WE2.R13.3: MAPPING VEGETATION STRUCTURE FROM UAVSAR TOMOGRAPHY USING 3-D CONVOLUTIONAL NEURAL NETWORKS
Michael Denbina, Richard Chen, Bryan Stiles, Naveen Ramachandran, Marc Simard, Yunling Lou, Sassan Saatchi, Jet Propulsion Laboratory, California Institute of Technology, United States
WE2.R13.5: ADDRESSING TROPICAL FOREST STRUCTURE CHANGES WITH SAR TOMOGRAPHY: THE AFRISAR 2016 - GABONX 2023 CASE
Matteo Pardini, Roman Guliaev, Konstantinos Papathanassiou, Irena Hajnsek, German Aerospace Center (DLR), Germany
WE2.R13.6: Tropical and temperate forest characterization by parametric P-band SAR tomography with low dimensional models
Pierre-Antoine Bou, ONERA, France; Laurent Ferro-Famil, ISAE-SupAero, France; Frederic Brigui, ONERA, France; Yue Huang, Meteo France, France
WE2.R13.7: ANALYSIS OF A DEEP LEARNING SOLUTION FOR TOMOSAR FOREST RECONSTRUCTION
Wenyu Yang, Giampaolo Ferraioli, Xialei Lu, Vito Pascazio, Gilda Schirinzi, Sergio Vitale, Università degli studi di Napoli, Italy; Hossein Aghababaei, University of Twente, Netherlands
