IGARSS 2026 || Washington, D.C. || 9

TU3.R9: Physics-Informed Machine Learning in Remote Sensing (1/4)

Tuesday, 11 August, 13:45 - 15:00

Location: Fairchild

Session Type: Oral

Session Co-Chairs: Davide De Santis, and Grigorios Tsagkatakis,

Track: Community Contributed Themes

Tue, 11 Aug, 13:45 - 14:00

TU3.R9.1: Physics-Integrated Deep Learning for Electromagnetic Inversion with Numerical Gradients: An LWD Case Study

Zhongrui Wang, University of Houston, United States; Hanming Wang, Chevron Technical Center, United States; Xiaotian Wang, Jiarui Lu, Jianfeng Zheng, University of Houston, United States

Tue, 11 Aug, 14:00 - 14:15

TU3.R9.2: Physics-Guided VAE with Refinement Networks and Extended Latent Space for Improved LAI Estimation

Kevin De Sousa, Jordi Inglada, Centre d’Etudes Spatiales de la Biosphère, France

Tue, 11 Aug, 14:15 - 14:30

TU3.R9.3: PHYSICS-INFORMED NEURAL NETWORKS FOR HYDROLOGICALLY DRIVEN SLOW-MOVING LANDSLIDE DISPLACEMENT MODELING USING INSAR TIME SERIES

Zhe Zhang, Xiaochuan Tang, Xuanmei Fan, Lei Zhang, Yifan Feng, Zhenlei Wei, Qing Pan, Chengdu University of Technology, China; Daniel Kibirige, University of Cape Town, South Africa; Filippo Catani, University of Padova, Italy

Tue, 11 Aug, 14:30 - 14:45

TU3.R9.4: PF-TRANS: PHYSICS-EMBEDDED FREQUENCY-AWARE TRANSFORMER FOR SPECTRAL RECONSTRUCTION

Yuzhe Gui, Tianzhu Liu, Yanfeng Gu, Xian Li, Harbin Institute of Technology, China

Tue, 11 Aug, 14:45 - 15:00

TU3.R9.5: PICSRL: PHYSICS-INFORMED CONTEXTUAL SPECTRAL REINFORCEMENT LEARNING

Mitra Nasr Azadani, Syed Usama Imtiaz, Nasrin Alamdari, Florida State University, United States