TU3.R2.4

Towards the understanding of the C-Band temporal signature of boreal forest through physiology parameters retrieval from Sentinel-1 Time Series and Machine Learning

Thomas Di Martino, CentraleSupelec, ONERA, France; Regis Guinvarc'h, Laetitia Thirion-Lefevre, CentraleSupelec, France; Elise Colin, ONERA, France
Session:
TU3.R2: Deep Learning Methods for Inversion Oral
Track:
Data Analysis
Location:
Room C
Presentation Time:
Tue, 18 Jul, 13:36 - 13:48 Pacific Time (UTC -8)
Session Chair:
John Kerekes, Rochester Institute of Technology
Session Manager:
Ge Jiang
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Session TU3.R2
TU3.R2.1: DEEP LEARNING APPROACH FOR MICROWAVE INTERFEROMETRY IMAGE RECONSTRUCTION: APPLICATION TO THE SMOS SATELLITE
ALI KHAZAAL, RDIS Conseils, France; Richard Faucheron, Nemesio Rodriguez-Fernandez, CESBIO, France; Louise Yu, CNES, France; Eric Anterrieu, CESBIO, France
TU3.R2.2: TRANSFORMER AND CNN HYBRID NEURAL NETWORK FOR SEISMIC IMPEDANCE INVERSION
Chunyu Ning, Bangyu Wu, Xi'an Jiaotong University, China; Zhaolin Zhu, Hainan institute of Zhejiang University, China
TU3.R2.3: SPARSE DOA ESTIMATION BASED ON A DEEP UNFOLDED NETWORK FOR MIMO RADAR
Haoyang Tang, Yongchao Zhang, Jiawei Luo, Yin Zhang, Yulin Huang, Jianyu Yang, University of Electronic Science and Technology of China, China
TU3.R2.4: Towards the understanding of the C-Band temporal signature of boreal forest through physiology parameters retrieval from Sentinel-1 Time Series and Machine Learning
Thomas Di Martino, CentraleSupelec, ONERA, France; Regis Guinvarc'h, Laetitia Thirion-Lefevre, CentraleSupelec, France; Elise Colin, ONERA, France
TU3.R2.5: PARAMETER RETRIEVAL OF ATTRIBUTED SCATTERING CENTER IN SAR IMAGE USING REINFORCEMENT LEARNING
Jiacheng Chen, Bo Long, Haipeng Wang, Fudan University, China
Resources
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