Flood Mapping Using Modified U-NET from TerraSAR-X Images

Jin Woo Yu, Young Woong Yoon, Eu Ru Lee, Won Kyung Baek, Hyung Sup Jung

Research output: Contribution to journalArticlepeer-review

5 Scopus citations


The rise in temperature induced by global warming caused in El Nino and La Nina, and abnormally changed the temperature of seawater. Rainfall concentrates in some locations due to abnormal variations in seawater temperature, causing frequent abnormal floods. It is important to rapidly detect flooded regions to recover and prevent human and property damage caused by floods. This is possible with synthetic aperture radar. This study aims to generate a model that directly derives flood-damaged areas by using modified U-NET and TerraSAR-X images based on Multi Kernel to reduce the effect of speckle noise through various characteristic map extraction and using two images before and after flooding as input data. To that purpose, two synthetic aperture radar (SAR) images were preprocessed to generate the model’s input data, which was then applied to the modified U-NET structure to train the flood detection deep learning model. Through this method, the flood area could be detected at a high level with an average F1 score value of 0.966. This result is expected to contribute to the rapid recovery of flood-stricken areas and the derivation of flood-prevention measures.

Original languageEnglish
Pages (from-to)1709-1722
Number of pages14
JournalKorean Journal of Remote Sensing
Issue number6
StatePublished - Dec 2022


  • Deep-learning
  • Flood-detection
  • Image segmentation
  • TerraSAR-X
  • U-NET


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