Oil Spill Detection and Monitoring from Satellite Image
Materials and Methods
Figure 1 shows the algorithm of processing Synthetic Aperture Radar (SAR) image for detection and classification of oil spill. A SAR data was selected for analysis, taken on 10 Oct. 1997 over the Straits of Malacca, covering around 110 x 150 km of Peninsula Malaysia from the states of Johor up to Malacca. The weather condition was extracted from ground stations over the study area. The maximum speed of seawater was reported around 0.25 m/s and the maximum for wind was 1.4 m/s.
Figure 1 Oil spill detection and classification algorithm for SAR image
Picture Analysis, Correction, and Enhancement (PACE) are a group of application programs in PCI image analysis software, which providing extensive digital image processing functions. These programs have been used to process radar data for detecting oil spill in water body.
Pre-processing of radar image include Antenna pattern correction (APC), Radiometric correction, and Geometric correction; have been applied to prepare correct data from SAR image.
Antenna Pattern Correction (APC) performs a radiometric balancing on synthetic aperture radar data to compensate for non-uniform illumination in the range direction due to the antenna pattern. Then the image has been geocoded by GCPWork interface in PCI image analysis software using image to map geocoding method. In this method the uncorrected image was selected and suitable georeference system has been defined for the image, then by choosing “Collect GCPs” command on PCI’s GCPWork, the “GCP Selection and Editing” panel loaded to select ground control points (GCP) by using topographic maps of the study area.
Post processing of radar image for detection of oil spill is including of image enhancement, texture analysis, dark slick detection, feature extraction, scaling and filtering. First the calibrated radar brightness image was generated from a Radarsat SAR image through SARBETA function in PACE interface. SARBETA generates a radar brightness channel from the input scaled radar channel using the gain offset and scaling. Then a set of texture was calculated for all pixels on the image through the texture analysis function in PACE interface. The measurements were based on second-order statistics computed from the gray level co-occurrence matrices. The textures measured after scaling have been used as input channels for classification algorithm.
To extract detailed information about oil spill, scaling radar standardized the texture values. Image Gray Level Scaling (SCAL) program in PACE, performs a linear or nonlinear mapping of the image gray levels to a desired output range. This program is typically used to scale data from "high" resolution (32 and 16-bit) channels to "low" resolution (16 and 8-bit) channels. These channels are then applied for oil spill image classification based on texture analysis results.
The image was then preceded to classify oil spill using supervised maximum likelihood algorithm. Speckles appearing on SAR images are natural phenomenon generated by the coherent processing of radar echoes (Lee 1986). The presence of speckle not only reduces the interpreter's ability to resolve fine detail, but also makes automatic segmentation of such images difficult. The gamma map filter is primarily used on radar data to remove high frequency noise. Finally to create automatic detection engine for any oil spill radar images PCI’s Modeler programming interface has been used to provide an interactive methodology for the development of all described oil spill image analysis and processing flows.