Remote Identification of Oil Films on Water Via Laser-Induced Fluorescence LiDAR

Laser-induced fluorescence lidar (LIF-LiDAR) is an effective technology for the stand-off detection, identification, and quantification of oil spills on the water surface. In this work, by using a self-developed LIF-LiDAR system, the laser-induced spectra of different oils with different thicknesses on the water surface were remotely measured at a distance of 100 m. The traditional algorithms based on the similarity degree of matching spectra are adopted to identify the oil types, which results in the 65% (min = 33.68%) and 85% (max = 98.66%) average matching accuracy when the pure oil spectra (infinite thickness) and the average oil spectra (different thickness) are introduced as the database, respectively. Furthermore, to avoid the influence of the pretreatment process in the traditional matching method, the machine learning (ML) model is applied to classify the oil types and an identifying accuracy of nearly 100% is successfully achieved. Our results presented in this work not only demonstrate the good performance of the classification models of fine tree, linear discriminant analysis (LDA), support vector machine (SVM), ${k}$ -nearest neighbor (KNN), and neural network (NN) in remote oil-type identification but also the capability of the combination of LIF-LiDAR and ML algorithms on the high-accuracy identification in the types of oil films on water.