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Flood Monitoring from SAR Data

Conference paper
Part of the NATO Science for Peace and Security Series C: Environmental Security book series (NAPSC)

Abstract

This paper presents the intelligent techniques approach for flood ­monitoring using Synthetic Aperture Radar (SAR) satellite images. We applied ­artificial neural networks and Self-Organizing Kohonen Maps (SOMs), to SAR image segmentation and classification. Our approach was used to process data from ­different SAR satellite instruments (ERS-2/SAR, ENVISAT/ASAR, RADARSAT-1/2) for different flood events: Tisza River, Ukraine and Hungary in 2001; Huaihe River, China in 2007; Mekong River, Thailand and Laos in 2008; Koshi River, India and Nepal in 2008; Norman River, Australia in 2009; Lake Liambezi, Namibia in 2009; Mekong River, Laos in 2009. This approach was implemented using Sensor Web paradigm for integrated system for flood monitoring and management.

Keywords

Flood Synthetic Aperture Radar (SAR) Artificial neural networks Sensor Web paradigm 

Notes

Acknowledgments

This work is supported by ESA CAT-1 project “Wide Area Grid Testbed for Flood Monitoring using Spaceborne SAR and Optical Data” (No. 4181), and by joint project of the Science and Technology Center in Ukraine (STCU) and the National Academy of Sciences of Ukraine (NASU), “Grid Technologies for Multi-Source Data Integration” (No. 4928).

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Copyright information

© Springer Science+Business Media B.V. 2011

Authors and Affiliations

  • Nataliia Kussul
    • 1
  • Andrii Shelestov
    • 1
  • Sergii Skakun
    • 1
  1. 1.Space Research Institute NASU-NSAUKyivUkraine

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