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.

Road extraction offers great potential for research initiatives because of the complexity due to its great topological variability. The use of remote sensing imagery to accomplish this mapping is an interesting option. Indeed, satellite images can be acquired shortly after the event, and cover a large area of territory. We hope to produce a mapping of the present facilities from very high resolution images shortly after a disaster. This availability of very high spatial resolution images brings added value to the study in urban areas and their mapping. Increasing the spatial resolution generates noise, which makes extraction difficult, especially in the event of an earthquake in an urban context. This problem increases false alarm rates and generally affects the performance of road extraction algorithms in detecting linear features used to locate and extract roads on such images. During major disasters, short deadlines demand an effective response in terms of updating the mapping of affected areas. Our aim is to improve the road extraction quality after adaptation of Lowe’s scale-invariant features transform descriptors jointly with spectral angle algorithms. An illustration is performed on three high-resolution images, respectively, representing a rural, suburban, and urban disaster area, captured by the Quickbird satellite. Our approach significantly reduces the false detection rate and shows an increase in overall quality of up to nearly 30% in some cases as compared to what obtain in the literature.