Identification of marine oil spill pollution using hyperspectral combined with thermal infrared remote sensing

Abstract

The types of marine oil spill pollution are closely related to source tracing and pollution disposal, which is an important basis for oil spill pollution punishment. The types of marine oil spill pollution generally include different types of oil products as well as crude oil and its emulsions in different states. This paper designed and implemented two outdoor oil spill simulation experiments, obtained the hyperspectral and thermal infrared remote sensing data of different oil spill pollution types, constructed a hyperspectral recognition algorithm of oil spill pollution type based on classical machine learning, ensemble learning and deep learning models, and explored to improve the identification ability of hyperspectral oil spill pollution type by adding thermal infrared features. The research shows that hyperspectral combined with thermal infrared remote sensing can effectively improve the recognition accuracy of different oils, but thermal infrared remote sensing cannot be used to distinguish crude oil and high concentration water-in-oil emulsion. On this basis, the recognition ability of hyperspectral combined with thermal infrared for different oil film thicknesses is also discussed. The combination of hyperspectral and thermal infrared remote sensing can provide important technical support for emergency response to maritime emergencies and oil spill monitoring business of relevant departments.