| 光谱学与光谱分析 |
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| Study of Automatic Marine Oil Spills Detection Using Imaging Spectroscopy |
| LIU De-lian, HAN Liang, ZHANG Jian-qi |
| School of Technical Physics, Xidian University, Xi’an 710071, China |
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Abstract To reduce artificial auxiliary works in oil spills detection process, an automatic oil spill detection method based on adaptive matched filter is presented. Firstly, the characteristics of reflectance spectral signature of C—H bond in oil spill are analyzed. And an oil spill spectral signature extraction model is designed by using the spectral feature of C—H bond. It is then used to obtain the reference spectral signature for the following oil spill detection step. Secondly, the characteristics of reflectance spectral signature of sea water, clouds, and oil spill are compared. The bands which have large difference in reflectance spectral signatures of the sea water, clouds, and oil spill are selected. By using these bands, the sea water pixels are segmented. And the background parameters are then calculated. Finally, the classical adaptive matched filter from target detection algorithms is improved and introduced for oil spill detection. The proposed method is applied to the real airborne visible infrared imaging spectrometer (AVIRIS) hyperspectral image captured during the deepwater horizon oil spill in the Gulf of Mexico for oil spill detection. The results show that the proposed method has, high efficiency, does not need artificial auxiliary work, and can be used for automatic detection of marine oil spill.
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Received: 2013-05-14
Accepted: 2013-07-29
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Corresponding Authors:
LIU De-lian
E-mail: dlliu@xidian.edu.cn
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[1] Brekkea C, Solberg A H S. Remote Sensing of Environment, 2005, 95(1): 1.
[2] Jha M N, Levy J, Cao Y. Sensor, 2008, 8(1): 236.
[3] Otremba Z, Piskozub J. Opt. Express, 2004, 12(8): 1671.
[4] Otremba Z, Piskozub J. Opt. Express, 2003, 11(1): 2.
[5] Salem F, Kafatos M, El-ghazawi T, et al. International Journal of Remote Sensing, 2005, 26(4): 811.
[6] Hu C, Li X, Pichel W G, et al. Geophysical Research Letters, 2009, 36(1): L01604.
[7] Leifer I, Lehr W J, Simecek-Beatty D, et al. Remote Sensing of Environment, 2012, 124(9): 185.
[8] Kokaly R F, Hoefen T M, Livo K E，et al. U. S. Geological Survey Open-File Report 2010-1107, 2010: 1.
[9] Rand R S, Clark R N, Livo K E. Proc. of SPIE, 2011, 8158: 81580N-1-12.
[10] Manolakis D, Shaw G. IEEE Signal Process. Mag., 2002, 19(1): 29.
[11] Chen W S, Reed I S. Digital Signal Process，1991, 1(4): 198. |
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