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Proceedings Paper

Estimating oil thickness by simplified Hapke model
Author(s): Mei-ping Song; Ren-chao Lin; Ju-bai An; Hai-mo Bao
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Paper Abstract

As the hyperspectral image combines spacial information with spectral information, the spectroscopic data can describe the characteristics of surface feature more accurately, and provide possibilities for classification and quantitative calculation for the surface features. Now, the unmixing technology for mixed pixel has become a hot topic in this field. The technology for pixel unmixing contains two main directions. The first one is based on linear mixing model. This model assumes that the pixel is formed by endmembers according to linear relationship. The methods based on this model are easy to be implemented. But the ideal model can’t describe the mixed relation of the surface features accurately. So the accuracy of abundance estimation can’t be guaranteed. The second one is based on non-linear model. This method could get good analytical results, but they are mainly established for particular surface features and difficult for implementation. This paper was mainly aimed at the research of abundance estimation. A simplified Hapke model is proposed to be applied to actual hyperspectral image of oil spilling, so as to obtain the estimation of oil thickness. The Hapke model could transform the non-linear relationship to linear relationship for hyperspetral data. The spectral reflectances of non-linear relationship are transformed to spectral albedo satisfying linear relationship, without changing the abundance. At last, this model is applied to actual hyperspectral image of oil spilling, achieving estimation for oil thickness.

Paper Details

Date Published: 30 August 2013
PDF: 7 pages
Proc. SPIE 8910, International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Spectrometer Technologies and Applications, 891025 (30 August 2013); doi: 10.1117/12.2035049
Show Author Affiliations
Mei-ping Song, Dalian Maritime Univ. (China)
Ren-chao Lin, Dalian Maritime Univ. (China)
Ju-bai An, Dalian Maritime Univ. (China)
Hai-mo Bao, Dalian Nationalities Univ. (China)


Published in SPIE Proceedings Vol. 8910:
International Symposium on Photoelectronic Detection and Imaging 2013: Imaging Spectrometer Technologies and Applications
Lifu Zhang; Jianfeng Yang, Editor(s)

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