Oil spill detection from RISAT-1 imagery using texture analysis

Oil spill pollution is a major environmental concern since it can cause serious damage to marine ecosystem. Periodic monitoring and detection of oil spills and its movement is a challenging task for clean-up and recovery operations. Over past few years Synthetic Aperture Radar (SAR) based remote sensing has received a considerable attention for monitoring and detection of oil spill due to its unique capabilities to provide wide-area surveillance in all weather conditions. However, interpretation of marine SAR imagery is often ambiguous, since it is difficult to separate oil spill from some other look-alike features. In this paper, an attempt has been made to extract oil spill from RISAT-1 imagery. Texture analysis based on Gray Level Co-occurrence Matrix (GLCM) is carried out on RISAT-1 HH polarization imagery and further classification has been performed using Support Vector Machine (SVM) classifier to extract oil spills. Classified output is in well agreement with the visual interpretation and its characteristics in SAR image.

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