నైరూప్య

Oil Spill Detection Using Optimal Thresholding for MODIS Images

Aishwarya S, Jayageetha R.T, Leninisha S

Millions of gallons of oil restfully end up in the seas every year. The early detection and identification of oil spills are critical prerequisites for performing cost effective maritime salvage operations. To overcome this illicit pollution, an oil spill detection method is proposed by Satellite Image Processing Technology. This paper presents a new approach for distinguishing oil spills that are produced by stationary offshore sources, during their early phase of occurrence. The result was reached after analyzing over acquired data that were produced by passive optical sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS). The developed processing scheme involves optimal thresholding of the MODIS images by three algorithms namely Thresholding – By Index, By Value and By Function followed by the integration of all algorithms for detection of possible oil spills. Then segmentation is performed for the separation of look- alikes and oil spill features for the oil spill extraction. The validity of this new empirical algorithm, Thresholding - By Index, By Value, By Function depends upon the difference of RGB color and threshold value of an image. It can also be shown that unique texture differences can be revealed between oil spill and other features by applying Edge Detection Methodology and therefore the reduction of false positives is achieved. The developed algorithm’s efficiency for real time oil spill detection and monitoring was also tested for 93% accuracy and experimentally evaluated by the generation of reports.