Grid for flood extent extraction from SAR imagery

Efficient monitoring and prediction of floods and risk management is impossible without the use of Earth Observation (EO) data from space. Satellite observations enable acquisition of data for large and hard-to-reach territories, as well as continuous measurements. One of the important problems associated with flood monitoring is flood extent extraction, since it is impractical to acquire the flood area through field observations. Flood extent can be used for hydraulic models to reconstruct what happened during the flood and determine what caused the water to go where it did, for damage assessment and risk management, and can benefit to rescuers during flooding.
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. SOMs provide effective software tool for the visualization of high-dimensional data, automatically discover of statistically salient features of pattern vectors in data set, and can find clusters in training data pattern space which can be used to classify new patterns. The workflow of our approach is as follows: 1. Transformation of raw data to lat/long projection; (2) Image calibration; (3) Geocoding; (4) Image segmentation and classification using SOMs; (5) Image post-processing.
We tested our approach on data acquired from three different satellites: 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 demonstrated the efficiency of our approach.
This work is supported by ESA CAT-1 project “Wide Area Grid Testbed for Flood Monitoring using Spaceborne SAR and Optical Data” (No. 4181); joint project of INTAS, the Centre National d’Etudes Spatiales (CNES) and the National Space Agency of Ukraine (NSAU), “Data Fusion Grid Infrastructure” (Ref. Nr 06-1000024-9154); joint project of the Science & Technology Center in Ukraine (STCU) and the National Academy of Sciences of Ukraine (NASU), “Grid Technologies for Multi-Source Data Integration” (No. 4928), and the Ministry of Education and Science of Ukraine, “Development of Integrated Remote Sensing Data Processing System using Grid Technologies” (No. M/72-2008).