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Fusion of SAR and Multispectral Satellite Images Using Multiscale Analysis and Dempster-Shafer Theory for Flood Extent Extraction

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Monitoring flood extent by means of Synthetic Aperture Radar (SAR) images has become a very common practice among decision makers and planners in disaster management as these images provide wide area coverage in extreme weather conditions. However, due to the satellite revisit time, their availability hinders their efficient use in disaster management. To capitalize on SAR images characteristics, this work considers both SAR and optical multispectral (MS) images, and proposes a novel method for SAR and optical image fusion in application to flood extent monitoring, which is based on two main steps: 1- Extraction of water pixels from the pre- and post-flooding images using a Modified Water Index (MWI) for water bodies identification from optical MS images and the Structural Feature Set (SFS) texture measurement for homogeneous areas extraction from SAR images, and 2- Applying the Max-Tree structure to estimate mass functions based on the multiscale and the multishape analysis of the input features map which are subsequently incorporated into the fusion module using Dempster-Shafer theory (DST). The results obtained in the evaluation of the proposed fusion method for three flood events characterized by different satellite image scenarios demonstrate the benefits of the multiscale DST fusion strategy in terms of chosen metrics in the classification


 Existing Method:

  • Averagingand Maximization methods based spatial level fusion
  • Thresholding and K means clustering methods for segmentation:
  • Wavelet Transform



  • Contrast information loss due to averaging method
  • Maximization method sensitive to sensor noise and high spatial distortion
  • K means – It is not suitable for all lighting condition of images
  • Difficult to measure the cluster quality


 Proposed Method:

  • Dual Level Wavelet and Log Ration Transform
  • Detection of Back scattering Changes at Building Scale
  • Building Detection using NN classifier.
  • Dempster-Shafer theory (DST).


Block Diagram:

Fusion of SAR and Multispectral Satellite Images Using Multiscale Analysis



  • Accurate detection of foreground changes by fusion
  • Less sensitive to noises



  • Earth land changes detection in Satellite field
  • Medical field.
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