Detection of floods and heavy rain using Cosmo-SkyMed data: The event in Northwestern Italy of November 2011

In this work, an automatic method to distinguish, in X-band SAR images such as those supplied by Cosmo-SkyMed, water surfaces (either flooded, or permanent water bodies) from artifacts due to heavy precipitation, is designed to improve flood detection accuracy. The method, mainly based on the fuzzy logic, consists of two main steps, i.e., the detection of low backscatter areas and the classification of each dark object present in the considered SAR image. The algorithm uses ancillary data, such as a local incidence angle map and a Land Cover map. Through the fuzzy logic, it integrates different rules for the detection of low backscatter areas (based on the standard deviation of the backscattering coefficient and on a well-established radar backscattering model), as well as different rules for the classification of the low backscatter (dark) areas (i.e., to distinguish water surfaces from artifacts) based on their geometrical and shape features and on both land cover and local incidence angle.