Estimation of Marine Oil Spill Surface Characteristics Based on Laser-Induced Fluorescence With IA-DBSCAN

Detection of marine oil spills and accurate estimation of the oil spill concentration distribution are essential in an emergency response. This paper proposes an iterative adaptation density-based spatial clustering of applications with noise (IA-DBSCAN) algorithm, which aims to estimate the surface characteristics of marine oil spills, including source localization and concentration distribution. This algorithm addresses the issues of traditional DBSCAN algorithms, such as the inability to obtain density information and the lack of autonomy in parameter selection. We utilize Laser-Induced Fluorescence (LIF) technology to monitor marine oil spills. According to the Bernoulli model, we convert the collected fluorescence intensity into the density of fluorescent points. The density distribution of these fluorescent points can be used to estimate the characteristics of the oil spill surface. This brings other challenges: clustering under mixed density conditions and accurately calculating the concentration of spilled oil. To address these challenges, a mean filtering method based on the distance-weighted from oil source points was proposed to smooth the concentration of fluorescent points, which improves the estimation accuracy of the oil spill surface as well as reduces the fluorescence detection errors caused by wave and environmental factors. Finally, tests were conducted in both marine simulation environments and laboratory simulation environments. The results of the tests indicate that the proposed method is capable of accurately estimating the characteristics of the oil spill surface.