Detection and estimation of hazardous noxious substance thickness based on hyperspectral remote sensing

Jae-Jin Park; Kyung-Ae Park; Pierre-Yves Foucher; Tae-Sung Kim; Moonjin Lee

doi:10.1117/12.3011190

8 June 2024 Detection and estimation of hazardous noxious substance thickness based on hyperspectral remote sensing

Jae-Jin Park, Kyung-Ae Park, Pierre-Yves Foucher, Tae-Sung Kim, Moonjin Lee

Proceedings Volume 13031, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX; 130310M (2024) https://doi.org/10.1117/12.3011190
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

Conference Poster

Abstract

A hazardous noxious substance (HNS) spill accident is one of the most devastating maritime disasters as it is accompanied by toxicity, ﬁre, and explosions in the ocean. In this study, a ground HNS measurement experiment was conducted for artiﬁcially spilled HNS by using two hyperspectral cameras at VNIR and SWIR wavelengths. HNS images were obtained by pouring 1 L of toluene into an outdoor marine pool and observing it with a hyperspectral sensor installed at a height of approximately 12 m. The pure endmember spectra of toluene and seawater were extracted using principal component analysis and N-FINDR, and a Gaussian mixture model was applied to the toluene abundance fraction. Consequently, a toluene spill area of approximately 2.4317 m2 was detected according to the 36% criteria suitable for HNS detection. The HNS thickness estimation was based on a three-layer two-beam interference theory model. Considering the detection area and ground resolution, the amount of leaked toluene was estimated to be 0.9336L. This study is expected to contribute to the establishment of maritime HNS spill response strategies in the near future based on the novel hyperspectral HNS experiment.

Citation Download Citation

Jae-Jin Park, Kyung-Ae Park, Pierre-Yves Foucher, Tae-Sung Kim, and Moonjin Lee "Detection and estimation of hazardous noxious substance thickness based on hyperspectral remote sensing", Proc. SPIE 13031, Algorithms, Technologies, and Applications for Multispectral and Hyperspectral Imaging XXX, 130310M (8 June 2024); https://doi.org/10.1117/12.3011190

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