Performance Analysis of Detector Algorithms using Drone-Based Radar Systems for Oil Spill Detection

Large tankers collisions with rocky shoals, platform accidents, pipelines ruptures and operative discharges are main contributors to oil pollution in the World’s oceans. Consequently, the release of the petroleum pollutants into coastal waters harms severely the environmental ecosystem. According to the European Space Agency (ESA), an estimate of 4.5 million tons are spilled on annual basis worldwide. Therefore, having oil pollution monitoring system is something crucial for the preservation of the coastal ecosystem. Recent remote sensing techniques combine between aircrafts and satellite surveillance in order to increase the probability of early spill detection, as well as to cover large spill areas.

We are working on a project that targets eventually to incorporate MIMO radar on drone for oil spill detection. The project will provide a quick assessment tool for oil spill accidents similar to what happened in the summer of 2006 in Lebanon, where 15000 tons of heavy fuel oil spilled in the Mediterranean Sea. In addition, the MIMO radar drones will be prominent by its providence to high spectral resolution, its allowance to parallel scanning, and its relative low cost. The project is composed of three phases. The first phase deals with the problem formulation and treatment using theoretical calculation and numerical simulations. The second phase deals with the prototype leading to the product in the third phase. As researchers, we are focusing on the first and second phases. Experimental measurements conducted in the laboratories in France are used to validate the models, to study the effectiveness of the algorithms and to provide the prototype. The product development in the final phase is left for application engineers.

Specifically for this abstract, we develop radar’s algorithms that take into account both the mathematical and the physical modeling of the sea surface covered by oil slicks. In these detection algorithms, we use the statistical characterization of the power reflectivity and its distribution under various scenarios (noise levels, oil thicknesses and electromagnetic wave’ frequencies). We first propose a single frequency oil spill detector that uses multiple observations of power reflection coefficients over several scanning iterations for the sea area. Increasing the number of observations leads to an increase in the certainty of the detector. Second, we address the correctness and the effectiveness of this detector for different scenarios using Monte- Carlo simulations. Results show the inability of this detector to effectively distinguish between oil slicks and oil-free slicks. An upgrade of this detector is the multi-frequency multi-snapshot detector where several electromagnetic frequencies are used when scanning the area. Performance analysis of the second detector proves its ability to overcome the drawbacks of the first detector by providing accurate detection.