Automated flood detection with improved robustness and efficiency using multi-temporal SAR data

Abstract

Flood detection from synthetic aperture radar (SAR) images should be performed with accurate, stable, automated and time-efficient algorithms; however, few methods meet all these requirements. Recently, Giustarini et al. proposed an automated promising methodology, capable of providing satisfactory results in flood detection. The algorithm is based on the assumption that a flood image contains a relatively high number of pixels with low backscatter values, exhibiting a bimodal histogram. For the case of a histogram that is not bimodal, the optimization of the theoretical curve describing the water pixels has to be manually constrained in a user-defined range. To overcome this shortcoming, this letter proposes an alternative procedure for core water body identification. First, by thresholding the difference image, derived by change detection between the flood and reference images, a mask of core water bodies is identified. Then, the mask is applied on the flood image, to extract the water pixels located in the core water bodies and straightforwardly derive the statistical curve describing water pixels. Successively, a sequence of thresholding, region growing and change detection is applied. The experimental results with two pairs of SAR images show that the proposed automated algorithm is stable and time-efficient, and provides accurate results.

Funding

This work was supported by Chinese Scholarship Council [grant number 201304740123], the National Research Fund of Luxembourg and the Belgian Science Policy through the ‘FLOODMOIST project’ [SR/02/152] and the ‘PAPARAZZI project’ [CORE: C11/SR/1277979].