Abstract
Nowadays, satellite images are considered as one of the most relevant sources of information in the context of major disasters management. Their availability in extreme weather conditions and their ability to cover wide geographic areas make them an indispensable tool toward an effective disaster response. Among the various available sensors, Synthetic Aperture Radar (SAR) is distinguished in the context of flood management by its ability to penetrate cloud cover and its robustness to unfavourable weather conditions. This work aims at developing a new technique for flooded areas extraction from high resolution time-series SAR images. The proposed approach is mainly based on three steps: first, homogeneous regions characterizing water surfaces are extracted from each SAR image using a local texture descriptor. Then, mathematical morphology is applied to filter tiny artifacts and small homogeneous areas present in the image. And finally, spatial and radiometric information embedded in each pixel are extracted and are fused with the same pixel information but from another image to decide if the current pixel belongs to a flooded region. In order to assess the performance of the proposed algorithm, our methodology was applied to time-series images acquired before and during three different flooding events: (1) Richelieu River and lake Champlain floods, Quebec, Canada in 2011; (2) Evros River floods, Greece in 2014 and (3) Western and southwestern of Iran floods in 2016. Experiments show that our approach gives very promising results compared to existing techniques.
Highlights
Regardless of whether they originate from the overflow of water bodies such as lakes or rivers, or from the melting snow during spring, floods share in common their devastating impact on human lives, environment and infrastructure
By comparing the two images acquired at two different dates during the disaster, we find that only a small agricultural area has been added to the list of flooded areas
The second phenomenon that appears in the same scene is the appearance of a small flooded area in the bottom left corner of the image acquired at t2 despite the fact that the water level has dropped in the main river
Summary
Regardless of whether they originate from the overflow of water bodies such as lakes or rivers, or from the melting snow during spring, floods share in common their devastating impact on human lives, environment and infrastructure. Despite various efforts to reduce their socio-economic and financial impacts, these initiatives remain insufficient given the constant increase in the frequency of these phenomena due to climate changes affecting our planet In this context, remote sensing data has repeatedly shown its interest and usefulness during the various phases of flood management process [1,2,3] by providing an overview of the situation on the ground without direct contact with the flooded area and by allowing the decision makers to follow the water extent during the disaster. SAR data have been widely used to extract floods as it has the advantages of penetrating through the cloud cover and the possibility of mapping the affected region at any time of the day and night [7] The availability of these images has prompted worldwide researchers to propose new approaches allowing better exploitation of these data and minimizing the execution time of existing algorithms so that they meet the requirements in terms of rapid response and effective management
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