Research on Dual-Driven Identification of Oil-Spill Type Based on Optical and Thermal Characteristics

Marine oil spills pose a significant risk to the ecological balance and human health. It is crucial to promptly and accurately identify the type of oil spill to facilitate emergency response and inform scientific decisions. Remote sensing technology is at the forefront of current research on oil type identification. This article presented comprehensive research on the systematic identification of oil types. The optical and thermal infrared data were gathered for various typical oils to elucidate their optical and thermal characteristics (OTC). On this basis, we developed the oil-type OTC dual-driven identification model (OTC-DDIM). This model incorporates a sample expansion module [OTC-conditional generative adversarial network (CGAN)] to increase sample diversity, a characteristic extraction module (OTC-EM) to extract OTC, and an adaptive identification module to fuse and enhance OTC for identifying oil-spill types. Further research revealed the critical role of optical characteristic screening in eliminating redundant information interference and improving the identification accuracy and efficiency. Temperature, a dominant environmental factor (EF), played a key constraint on the generation of high-quality thermal infrared extension samples by OTC-CGAN. Under ideal oil-spill scenarios, the model demonstrated excellent identification capabilities, achieving an overall accuracy (OA) of 96.15%, with both Kappa and average $F_{1}$ -score reaching 0.96. The method verification and application were conducted under simulated oil-spill scenarios. The experimental results demonstrated that OTC-DDIM could accurately and reliably identify oil-spill types using OTC, achieving accuracies of 91.71%, 0.92, and 0.90, respectively. In summary, this study could provide essential technical support for emergency responses to marine oil-spill accidents.