Experimental Analysis on the Ultraviolet Imaging of Oil Film on Water Surface: Implication for the Optimal Band for Oil Film Detection Using Ultraviolet Imaging

Xie, Ming; Li, Ying

doi:10.1007/s00244-022-00934-7

Cited by 5 publications

(1 citation statement)

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“…In fact, some recent studies rethought and evaluated the feasibility of oil spill detection using passive UV imaging. According to the early studies on the transmissivity and reflectivity of liquid in UV bands (Bai et al, 2020; De Haan, 1993), Xie and Li (2022) examined the optical responses of oil films in UV bands and quantitatively evaluated the imaging quality under different wavelengths. The results inferred that the longer wavelength in UV‐A bands was more appropriate for oil spill detection because the UV light of short wavelength can penetrate through the oil films and detect the substance in the water, which would negatively affect the results of surface oil film detection.…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of thin oil films on water surfaces in ultraviolet imagery

Xie

Zhang

et al. 2023

The Photogrammetric Record

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Among the various remote sensing technologies that have been applied to monitor oil spills on the sea surface, passive ultraviolet (UV) imaging is a controversial one that has raised some disputes in the community of oil spill remote sensing. As a result, the research and applications of oil spill detection using passive UV imaging have not been as developed as other methods. In order to clarify some existing questions on oil spill detection using passive UV remote sensing technology, this paper discusses the needs of thin oil film detection, examines the feasibility of thin oil film detection using passive UV imaging through field experiments under controlled conditions and validates it with the UV imagery derived from the airborne visible/infrared imaging spectrometer (AVIRIS) observation of the Deepwater Horizon oil spill. Two types of fully automatic models are designed to extract the thin oil films on the water surface: (1) a binary classification model based on an adaptive threshold; (2) an unsupervised image segmentation model based on image clustering and greyscale histogram analysis. The two models are tested on the UV imagery obtained through both field experiments and AVIRIS observations. The results indicate that the binary classification model can extract the thin oil films with reasonable accuracy under stable imaging conditions, while the unsupervised image clustering model can robustly detect the thin oil films at the cost of higher computational complexity. These results infer that passive UV imaging is an effective way to detect thin oil films and could be applied to provide early warning at the beginning stage of oil spills and reduce further damage. It may also be applied as a supplementary method for oil spill detection to achieve comprehensive oil spill monitoring.

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Section: Introductionmentioning

confidence: 99%