MO3.PZZ.5
EXPLAINABLE AI FOR WETLAND MAPPING USING HIGH-RESOLUTION REMOTE SENSING DATA
Mohammad Marjani, Masoud Mahdianpari, Eric W. Gill, Memorial University of Newfoundland, Canada; Fariba Mohammadimanesh, Natural Resources Canada, Canada
Session:
MO3.PZZ: Student Paper Competition I Oral
Track:
Student Paper Competition
Location:
Plaza Auditorium
Presentation Time:
Monday, 4 August, 13:20 - 13:40
Session Co-Chairs:
Francesca Bovolo, Fondazione Bruno Kessler and Lianru Gao, Aerospace Information Research Institute, Chinese Academy of Sciences
Session MO3.PZZ
MO3.PZZ.1: A NOVEL AND PRACTICAL APPROACH TO GENERATE ALL-WEATHER 30-METER LAND SURFACE TEMPERATURE DATA
Shengjie Liu, Lu Zhang, Siqin Wang, University of Southern California, United States
MO3.PZZ.2: VALIDATION OF A NEW AIRBORNE SNOW RADAR ON ANTARCTIC SEA ICE
Pauline Barras, University of Canterbury, New Zealand; Adrian Eng-Choon Tan, Lincoln Agritech Ltd, New Zealand; Wolfgang Rack, University of Canterbury, New Zealand; Christian Haas, Alfred Wegener Institute, Germany; Inga Jane Smith, University of Otago, New Zealand; Sean Minhui Tashi Chua, Nansen Environmental and Remote Sensing Center, Norway; Shelley MacDonell, Daniel Price, University of Canterbury, New Zealand
MO3.PZZ.3: EARLY WARNING OF TSUNAMIS INTEGRATING ANOMALY DETECTION INFORMATION FROM HFSWR AND GNSS
Menghua Jiang, Yonggang Ji, Ruozhao Qu, Yahui Chen, China University of Petroleum (East China), China; Changjun Yu, Harbin Institute of Technology at Weihai, China
MO3.PZZ.4: PROPOSAL OF A CONTEXT-AWARE POLSAR FILTER BASED ON QUATERNION WAVELET SHRINKAGE FOR EFFICIENT LAND COVER CLASSIFICATION
Yuta Otsuka, Ryo Natsuaki, Akira Hirose, The University of Tokyo, Japan
MO3.PZZ.5: EXPLAINABLE AI FOR WETLAND MAPPING USING HIGH-RESOLUTION REMOTE SENSING DATA
Mohammad Marjani, Masoud Mahdianpari, Eric W. Gill, Memorial University of Newfoundland, Canada; Fariba Mohammadimanesh, Natural Resources Canada, Canada
Resources