Sentinel-1 spatiotemporal simulation using convolutional LSTM for flood mapping

• Main Authors: Ulloa, Noel Ivan, Yun, Sang-Ho, Chiang, Shou-Hao, Furuta, Ryoichi
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2022
• Subjects: Engineering::Electrical and electronic engineering; Spatiotemporal Simulation; Convolutional LSTM
• Online Access: https://hdl.handle.net/10356/163408
• Institution: Nanyang Technological University

The synthetic aperture radar (SAR) imagery has been widely applied for flooding mapping based on change detection approaches. However, errors in the mapping result are expected since not all land-cover changes are flood-induced, and those changes are sensitive to SAR data, such as crop growth or har...