TU4.R9.3
EXPLAINABLE NEURAL NETWORKS FOR AEROSOL RETRIEVAL: A PRUNING AND SHAP PERSPECTIVE
Davide De Santis, Marco Di Giacomo, Lorenzo Giuliano Papale, Giovanni Schiavon, Fabio Del Frate, "Tor Vergata" University of Rome, Italy
Session:
TU4.R9: Physics-Informed Machine Learning in Remote Sensing (2/4) Oral
Track:
Community Contributed Themes
Location:
TBD
Presentation Time:
Tue, 11 Aug, 16:45 - 17:00
Session Co-Chairs:
Davide De Santis, and Grigorios Tsagkatakis,
Presentation
Discussion
Resources
No resources available.
Session TU4.R9
TU4.R9.1: Set-Based Transformer for Atmospheric Compensation in Standoff LWIR Hyperspectral Imaging
Fabian Perez, Nicolas Quintero, Jeferson Acevedo, Hoover Rueda-Chacon, Universidad Industrial de Santander, Colombia
TU4.R9.2: Benchmarking Scientific Machine Learning Models for Air Quality Data
Venkata Sai Rahul Unnam, Khawja Imran Masud, Sahara Ali, University of North Texas, United States
TU4.R9.3: EXPLAINABLE NEURAL NETWORKS FOR AEROSOL RETRIEVAL: A PRUNING AND SHAP PERSPECTIVE
Davide De Santis, Marco Di Giacomo, Lorenzo Giuliano Papale, Giovanni Schiavon, Fabio Del Frate, "Tor Vergata" University of Rome, Italy
TU4.R9.4: A Physics-Consistent Reversible Calibration Framework for Multichannel Microwave Data Interpretation: A Case Study in Multi-Soil Parameter Retreival
Yuanhao Cao, Jiayi Du, Shurun Tan, International Campus, Zhejiang University, China
TU4.R9.5: MULTI-SOURCE UNCERTAINTY AWARE FUSION FOR SOIL MOISTURE ESTIMATION
Eleftherios Polychronakis, Foundation for Research and Technology - Hellas, Greece; Archana Kannan, James Campbell, Mahta Moghaddam, University of Southern California, United States; Panagiotis Tsakalides, Grigorios Tsagkatakis, Foundation for Research and Technology – Hellas, Greece
Contacts