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      https://www.ias.ac.in/article/fulltext/jess/122/03/0559-0572

    • Keywords

       

      Synthetic Aperture Radar; flood assessment; speckle filters; gray level co-occurrence matrix; mean shift segmentation.

    • Abstract

       

      Flood is one of the detrimental hydro-meteorological threats to mankind. This compels very efficient flood assessment models. In this paper, we propose remote sensing based flood assessment using Synthetic Aperture Radar (SAR) image because of its imperviousness to unfavourable weather conditions. However, they suffer from the speckle noise. Hence, the processing of SAR image is applied in two stages: speckle removal filters and image segmentation methods for flood mapping. The speckle noise has been reduced with the help of Lee, Frost and Gamma MAP filters. A performance comparison of these speckle removal filters is presented. From the results obtained, we deduce that the Gamma MAP is reliable. The selected Gamma MAP filtered image is segmented using Gray Level Co-occurrence Matrix (GLCM) and Mean Shift Segmentation (MSS). The GLCM is a texture analysis method that separates the image pixels into water and non-water groups based on their spectral feature whereas MSS is a gradient ascent method, here segmentation is carried out using spectral and spatial information. As test case, Kosi river flood is considered in our study. From the segmentation result of both these methods are comprehensively analysed and concluded that the MSS is efficient for flood mapping.

    • Author Affiliations

       

      J Senthilnath1 H Vikram Shenoy2 Ritwik Rajendra2 S N Omkar1 V Mani1 P G Diwakar3

      1. Department of Aerospace Engineering, Indian Institute of Science, Bangalore 560 012, India.
      2. Department of Electronics and Communication Engineering, National Institute of Technology Karnataka, Surathkal, Mangalore 575 025, India.
      3. Earth Observation System, ISRO Head Quarters, Bangalore, India.
    • Dates

       
  • Journal of Earth System Science | News

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