Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/1029

 Title: Intelligent Computations for Flood Monitoring Authors: Kussul, NataliiaShelestov, AndriiSkakun, Serhiy Keywords: Flood Extent ExtractionNeural NetworksData FusionSAR Images Issue Date: 2008 Publisher: Institute of Information Theories and Applications FOI ITHEA Abstract: Floods represent the most devastating natural hazards in the world, affecting more people and causing more property damage than any other natural phenomena. One of the important problems associated with flood monitoring is flood extent extraction from satellite imagery, since it is impractical to acquire the flood area through field observations. This paper presents a method to flood extent extraction from synthetic-aperture radar (SAR) images that is based on intelligent computations. In particular, we apply artificial neural networks, self-organizing Kohonen’s maps (SOMs), for SAR image segmentation and classification. We tested our approach to process data from three different satellite sensors: ERS-2/SAR (during flooding on Tisza river, Ukraine and Hungary, 2001), ENVISAT/ASAR WSM (Wide Swath Mode) and RADARSAT-1 (during flooding on Huaihe river, China, 2007). Obtained results showed the efficiency of our approach. URI: http://hdl.handle.net/10525/1029 ISSN: 1313-0455 Appears in Collections: Book 2 Advanced Research in Artificial Intelligence

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