2020 IEEE International Geoscience and Remote Sensing Symposium

September 26 - October 2, 2020 • Virtual Symposium

2020 IEEE International Geoscience and Remote Sensing Symposium

September 26 - October 2, 2020 • Virtual Symposium

WE2.R7.6

ON THE OPTIMAL DESIGN OF CONVOLUTIONAL NEURAL NETWORKS FOR EARTH OBSERVATION DATA ANALYSIS BY MAXIMIZATION OF INFORMATION EXTRACTION

Andrea Marinoni, UiT The Arctic University of Norway, Norway; Gianni Christian Iannelli, Ticinum Aerospace, Italy; Salman Khaleghian, UiT The Arctic University of Norway, Norway; Paolo Gamba, University of Pavia, Italy
Session:
Incorporating Physics into Deep Learning
Track:
Invited Sessions
Presentation Time:
Wed, 30 Sep, 15:30-15:40 (UTC)
Wed, 30 Sep, 23:30-23:40 China Standard Time (UTC +8)
Wed, 30 Sep, 17:30-17:40 Central Europe Summer Time (UTC +2)
Wed, 30 Sep, 08:30-08:40 Pacific Daylight Time (UTC -7)
Session Co-Chairs:
Katarina Doctor, U.S. Naval Research Laboratory and Jocelyn Chanussot, Univ. Grenoble Alpes; University of Iceland
Session Managers:
Ushasi Chaudhuri and Na Liu

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Session

WE2.R7.1: Physics-guided Machine Learning: Advances in an Emerging Paradigm Combining Scientific Knowledge with Machine Learning
Anuj Karpatne, Virginia Tech, United States
WE2.R7.2: PHYSICALLY INFORMED NEURAL NETWORKS FOR THE SIMULATION AND DATA-ASSIMILATION OF GEOPHYSICAL DYNAMICS
Said Ouala, Ronan Fablet, Lucas Drumetz, IMT-Atlantique, France; Bertrand Chapron, Ifremer, France; Ananda Pascual, IMEDEA, Spain; Fabrice Collard, Lucile Gaultier, ODL, France
WE2.R7.3: PROCESS GUIDED DEEP LEARNING FOR MODELING PHYSICAL SYSTEMS: AN APPLICATION IN LAKE TEMPERATURE MODELING
Xiaowei Jia, Jared Willard, University of Minnesota, United States; Anuj Karpatne, Virginia Tech, United States; Jordan Read, Jacob Zwart, USGS, United States; Michael Steinbach, Vipin Kumar, University of Minnesota, United States
WE2.R7.4: VISUALIZATION OF DEEP TRANSFER LEARNING IN SAR IMAGERY
Abu Md Niamul Taufique, Navya Nagananda, Andreas Savakis, Rochester Institute of Technology, United States
WE2.R7.5: EXPLORING THE RELATIONSHIPS BETWEEN SCATTERING PHYSICS AND AUTO-ENCODER LATENT-SPACE EMBEDDING
Shaunak De, Christian Clanton, Steven Bickerton, Oliwia Baney, Kaushik Patnaik, Orbital Insight Inc., United States
WE2.R7.6: ON THE OPTIMAL DESIGN OF CONVOLUTIONAL NEURAL NETWORKS FOR EARTH OBSERVATION DATA ANALYSIS BY MAXIMIZATION OF INFORMATION EXTRACTION
Andrea Marinoni, UiT The Arctic University of Norway, Norway; Gianni Christian Iannelli, Ticinum Aerospace, Italy; Salman Khaleghian, UiT The Arctic University of Norway, Norway; Paolo Gamba, University of Pavia, Italy
WE2.R7.7: BUILDING EXTRACTION BY GATED GRAPH CONVOLUTIONAL NEURAL NETWORK WITH DEEP STRUCTURED FEATURE EMBEDDING
Yilei Shi, Qinyu Li, Xiao Xiang Zhu, Technical University of Munich, Germany
WE2.R7.8: MULTI-SPECTRAL IMAGE CLASSIFICATION WITH QUANTUM NEURAL NETWORK
Piotr Gawron, Nicolaus Copernicus Astronomical Center, Polish Academy of Sciences, Poland; Stanisław Lewiński, Space Research Centre, Polish Academy of Sciences, Poland
WE2.R7.9: AN ENSEMBLE APPROACH FOR COMPRESSIVE SENSING WITH QUANTUM ANNEALERS
Ramin Ayanzadeh, Milton Halem, Tim Finin, University of Maryland, Baltimore County, United States