Multi-Condition Classification of Oil Spill in Ice Areas Based on Laser-Induced Fluorescence

Oil spill detection in ice-covered marine environments poses considerable challenges due to fluorescence signal interference from ice, heterogeneous surface properties, and environmental complexity. To address the lack of high-precision oil classification methods under such conditions, this study introduces a fluorescence-based multi-condition classification framework that integrates laser-induced fluorescence (LIF) spectroscopy with a machine learning model optimized by the Golden Sine Algorithm (Gold-SA). LIF spectra were collected for six oil types under four simulated ice coverage and oil volume scenarios, resulting in 24 distinct classification categories. Fluorescence signals underwent denoising using Savitzky-Golay (SG) filtering to improve signal stability and spectral reliability. The resulting Gold-SA-CatBoost model achieved 99.62% accuracy under laboratory conditions within the dataset and 100% accuracy in single-task oil-type identification, surpassing baseline models by a substantial margin. This work demonstrates the efficacy of integrating LIF with advanced optimization-based machine learning for robust oil spill detection under complex icy conditions. The proposed approach provides a viable fluorescence-based strategy for environmental monitoring in cold and polar marine regions.