Research on Cross-Spatiotemporal Remote Sensing Detection of Marine Oil Spills and Emulsions Based on Coupling Optical and Thermal Response Characteristics

Marine oil spills and emulsions are highly detrimental to the ecological environment and human health. Optical and thermal infrared remote sensing technologies provide important approaches for marine oil-spill detection, but accurate cross-spatiotemporal remote sensing detection in complex scenarios is still challenging. In this article, optical and thermal infrared observation experiments on oil spills and emulsions were designed and conducted to analyze the optical and thermal response characteristics and the technical feasibility of remote sensing detection. Then, a detection model coupling the optical and thermal response characteristics is proposed to break through the bottleneck of oil-spill detection across time and space in complex scenes, which can enhance the diversity of samples by exploiting the 3-D spatial-spectral feature (SSF) generation adversarial expansion module constrained by the sun glint intensity index (SGII). Meanwhile, based on the thermal infrared super-resolution enhancement module guided by texture features, the thermal infrared spatial geometric features of oil spills are improved, and the model’s ability to resist cloud and fog interference is enhanced. Besides, under the guidance of the optical feature guidance (OFG) module, the SSF information can be extracted based on the SSF convolution deep belief network (SSF-CDBN) to realize the oil-spill remote sensing migration detection both temporally and spatially. The results show that the model can accurately detect oil spills and emulsion type, the overall accuracy (OA) and Kappa are larger than 84.46% and 0.758 in ideal scenarios, 80.11% and 0.745 in complex scenarios, which is expected to provide new technical support for oil-spill accidents.