Fractal Dimension Algorithm for Oil Spill and Look-Alike Detections using RADARSAT-1 SAR and AIRSAR/POLSAR Data

  • Marghany, M., Cracknell, A. P. and Hashi M


Automatic detection of oil spill and look-alikes in synthetic aperture radar (SAR) is required standard procedures. In fact, oil spill and look-alike are appeared as dark patches in SAR data. This work utilizes a modification of the formula of the fractal box counting dimension in which divided a convoluted line of slick embedded in SAR data into small boxes. The method is based on the utilization of the probability distribution formula in the fractal box count. The purpose of this method is to use it for the discrimination of oil spill areas from the surrounding features e.g., sea surface and look-alikes in SAR data i.e., RADARSAT-1 SAR S2 mode and AIRSAR/POLSAR data The results show that the modified formula of the fractal box counting dimension is able to discriminate between oil spills and look-alike areas. The low wind area has the highest fractal dimension peak of 2.9, as compared to the oil slick and the surrounding rough sea. Further, modified formula of fractal box counting dimension is also able to detect look-alikes and low wind zone areas in AIRSAR/POLSAR data. It is interesting to find out that oil spill is absent in AIRSAR/POLSAR data. Both SAR data have maximum error standard deviation of 0.45 which performs with fractal dimension value of 2.9. In conclusion, modification formula of fractal box counting dimension is promising method for oil spill automatic detection in different sensor of SAR data.