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Detection and visualization of oil spill using thermal images

During the past two decades, Oil Spill Detection (OSD) received widespread attention from research communities. Both detection and analysis of OSD have fundamental importance for improving the efficiency of maritime environment ecosystems. Most recently, thermal imaging devices are used for oil detection and disaster management projects since they can provide spilling information at Day/Night time and can work under adverse weather conditions. Nevertheless, the quality of these images are poor, they are noisy, blurry, and they have low resolution. As well as a thermal image contrast between oil and water is often so small, that makes OSD problematic and challenging. The goal of this paper is to automatically detect and analyze the OSD on the upper sea/ocean layer that may help in the visualization of oil spills for disaster management purpose. For the purposes of comparison, quantitative and qualitative analysis was conducted on the existing segmentation approaches, namely OTSU, and Sauvola by using two new databases composed each of 100 diversified images extracted from 2 different videos. The performance of the proposed also evaluated by examining statistical measures of boundary error (BE), probabilistic rand index (PRI), variation of information (VI), global consistency error (GCE), and structural similarity index (SSI). The obtained results proved that the proposed method is more robust, accurate, and straightforward. Future research recommendations and conclusions are presented.