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Oil Spill Detection and Monitoring from Satellite Image
Figure 8 and 9 described the best results for automatic detection of oil spill in SAR image based on SCAL program, gamma image filtering and texture analysis program. The results where then saved on a new channel. In this images the edge of spilled oil is clearly presented and the spill look-likes are also eliminated.
Figure 8 Classified oil spill image by visual modeler in two regions; oil spills area in red and polluted area in black color.
Figure 9 Grey values of original image (channel 1) and attribute values after Homogeneity (Channel 2) and Angular Second Moment (Channel 3) texture analysis. Channel 2 specified the all polluted area and channel 3 rectified only very high-polluted area (spilled area).
Maximum Likelihood was found as the best algorithm for oil spill classification. Figure 10 shows the composite image classification results using texture analysis and scaled grey level image. Classification analysis results where given the important information about oil spill thickness and area of spillage. This figure presents the oil spill in three different classes according to thickness of the spilled oil.
Figure 10 SAR image classification and attribute DN values of the image pixels showing three different classes for oil spill.
Conclusion
Remotely sensed data are used for detecting oil spill to support the contingency plan at a specific location in Straits of Malacca. Several methods of image processing are applied for this task such as Gamma distribution analysis, texture analysis, image composite analysis and image classification. After pre processing (radiometric correction, APC, geometric correction, pixel size conversion) the image was applied to detect oil spill and its characteristics. This paper was focused on automatic dark slick detection and classification as early warning system for oil spill contingency planning.
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