Fine-Grained Oil Types Identification Based on Reflectance Spectrum: Implication for the Requirements on the Spectral Resolution of Hyperspectral Remote Sensors

Xie, Ming; Liu, Ying; Dong, Shuang; Zhang, Baochen; Gou, Tao

doi:10.1109/lgrs.2022.3176494

Cited by 9 publications

(1 citation statement)

References 28 publications

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“…Alternatively, optical remote sensing technologies distinguish the oil polluted seawater from clean seawater by their different optical properties (Hu et al, 2021; Otremba, 2000), for example, reflectance, absorption and scattering. Optical remote sensing data of various electromagnetic bands can be utilised to identify oil types (Li et al, 2021; Xie, Li, et al, 2022; Yang et al, 2021) or estimate oil thickness (Kieu & Law, 2022; Lu et al, 2013; Sun et al, 2016).…”

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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