Flood Extent Mapping from Time-Series SAR Images Based on Texture Analysis and Data Fusion

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.

Validation of flood risk maps using open source optical and radar satellite imagery

“…Moreover, the values of this metric increase with the return period of a flood, hence it is highest for a flood with a 500-year return period in both cases. The values of the recall metric for the validation of the 1997 flood in Badajoz are slightly higher than those obtained by Ouled Sghaier, Hammami, Foucher, and Lepage (2018).…”

The Use of Unmanned Aerial Vehicles to Estimate Direct Tangible Losses to Residential Properties from Flood Events: A Case Study of Cockermouth Following the Desmond Storm

“…A number of factors can curtail the effectiveness of remote sensing methods: (i) spatio-temporal coverage may not be available for the required zone and period, (ii) optical imagery cannot provide information if there is low cloud cover [5][6][7], and (iii) satellite (SAR) data, which can penetrate cloud cover, has an oblique viewing angle which makes it difficult to discriminate the water signature from other urban features [8]. Current remote sensing approaches fail to provide sufficient detail to assess the effects of micro-topography and the presence of property flood resistance measures.…”

Sentinel-1 Based Flood Mapping Using Interferometric Coherence and Intensity Change Detection Approach

“…In sense of feature selection to define and model the change between two scenes; there are several methods used in literature both for pixel-based or texture-based models (Ouled Sghaier et al, 2018) and (J Li et al, 2018). Commonly used change indicators for pixel-based models are the ratio, log-ratio (Y or difference operator, which suppresses the background information to extract the changed region.…”