TUP.P18: Machine Learning for Radar and SAR Application
Tue, 18 Jul, 14:15 - 15:45 Pacific Time (UTC -7)
Location: Poster Area 18
Session Type: Poster
Session Co-Chairs: Ronny Hänsch, German Aerospace Center and Ioannis Prapas, National Observatory of Athens and Ryo Natsuaki, The University of Tokyo
Track: AI and Big Data

TUP.P18.1: DEEP LEARNING FOR POLARIMETRIC RADAR QUANTITATIVE PRECIPITATION ESTIMATION: MODEL INTERPRETATION AND EXPLAINABILITY
Wenyuan Li, Lei Han, Ocean University of China, China; Haonan Chen, Colorado State University, United States

TUP.P18.2: INSAR PHASE FILTERING BY ATTENTION-BASED RESERVOIR COMPUTING FOR HIGH RELIABILITY
Bungo Konishi, Akira Hirose, Ryo Natsuaki, The University of Tokyo, Japan

TUP.P18.3: SEA CLUTTER SUPPRESSION FOR MARINE SURVEILLANCE RADAR BASED ON GENERATIVE ADVERSARIAL LEARNING
Jifang Pei, Yu Yang, Zhihao Fang, Weibo Huo, Peng Chen, Wenjing Wang, Jianyu Yang, University of Electronic Science and Technology of China (UESTC), China

TUP.P18.4: PREDICTION OF INSAR URBAN SURFACE TIME-SERIES DEFORMATION USING DEEP NEURAL NETWORKS
Yuhang Liu, Tao Chen, Jun Li, China University of Geosciences, China

TUP.P18.5: DEEP NEURAL NETWORKS FOR EVALUATING MICROWAVE SOUNDER DESIGNS
James MacKinnon, Antonia Gambacorta, Jeffrey Piepmeier, Mark Stephen, Joseph Santanello, Ian Adams, NASA Goddard Space Flight Center, United States; Rachael Kroodsma, Isaac Moradi, University of Maryland/Goddard Space Flight Center, United States; Greg Blumberg, Millersville University, United States; John Blaisdell, Science Applications International Corporation/Goddard Space Flight Center, United States; Jie Gong, ERT, Inc, United States; Alexander Kotsakis, ERT, Inc./Goddard Space Flight Center, United States; Ashley Wheeler, Science Applications International Corporation /Goddard Space Flight Center, United States

TUP.P18.6: DEEP LEARNING FOR FOREST CANOPY HEIGHT ESTIMATION FROM SAR
Ragini Mahesh, Ronny Hänsch, German Aerospace Center, Germany

TUP.P18.7: Inversion and prediction of time-varying surface subsidence in coal mines by combining SBAS-InSAR and time-series prediction algorithms
XiaoWei Guo, Tao Chen, Jun Li, China University of Geosciences, China

TUP.P18.8: GENERATING HIGH RESOLUTION SAR IMAGES WITH LOSS FUNCTION CUSTOMIZATION
Lee Jung-Hoon, Kim Junwoo, Kim Duk-Jin, School of Earth and Environmental Sciences, Seoul National University, South Korea; Kim Jae-Hyun, Department of Electrical and Computer Engineering, Ajou University, South Korea

TUP.P18.9: ESTIMATING NDVI FROM SAR IMAGES USING CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS
Pietro Soglia, Paula Gonzalez, Izar Azpiroz, Urtzi Otamendi, Marco Quartulli, Vicomtech Foundation, Italy; Sergio Salata, AVS Added Value Industrial Engineering Solutions, Spain

TUP.P18.10: INTEGRATING ICOPS TIME-SERIES INSAR MEASUREMENT WITH THE CONVOLUTIONAL NEURAL NETWORK (CNN) AND OPTIMIZED HOT SPOT ANALYSIS (OHSA) TO MONITOR LAND SUBSIDENCE IN PEKALONGAN, INDONESIA
Wahyu Luqmanul Hakim, Muhammad Fulki Fadhillah, Bongchan Kim, Sungjae Park, Chang-Wook Lee, Kangwon National University, South Korea

TUP.P18.11: DEEP LEARNING-BASED LIKELIHOOD PHASE UNWRAPPING FOR MULTI-BASELINE INSAR INTERFEROGRAMS
Lifan Zhou, Changshu Institute of Technology, China; Hanwen Yu, Yong Wang, University of Electronic Science and Technology of China, China; Mengdao Xing, Xidian University, China

TUP.P18.12: A CNN-based interferogram filtering approach to enhance the co-seismic surface displacements identification by exploiting the EPOSAR DInSAR maps global archive
Adele Fusco, Sabatino Buonanno, Giovanni Zeni, Fernando Monterroso, Gloria Bordogna, Paola Carrara, Manuela Bonano, Ivana Zinno, Giovanni Onorato, Claudio De Luca, Francesco Casu, Michele Manunta, Riccardo Lanari, CNR, Italy; Simone Atzori, INGV, Italy; Muhammad Yasir, CNR-IREA,Parthenope University, Italy