Extraction method of oil spill information using airborne hyper-spectral remote sensing data

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Journal of Dalian Maritime University ›› 2014, Vol. 40 ›› Issue (1) : 89-92.

LIU Bing-xin, ZHANG Zhi-da, LI Ying, CHEN Peng

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Abstract

Decision tree classification method was proposed on basis of the minimum noise fraction(MNF) to reduce dimensions of hyper-spectral remote sensing data and improve processing efficiency. The data redundancy was reduced by means of MNF, and the figure noise was separated. The decision tree was established according to analyzing landmarks’ MNF eigenvalue, and the relative thickness of the oil film was extracted. The results show that the method mentioned could ensure recognition accuracy, achieve effective use of spectral dimension information, as well as reduce the processing time significantly, so as to make the quick products for oil spill response by using hyper-spectral data.

Key words

hyper-spectral remote sensing / oil spill monitoring / minimum noise fraction(MNF)

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LIU Bing-xin, ZHANG Zhi-da, LI Ying, CHEN Peng. Extraction method of oil spill information using airborne hyper-spectral remote sensing data[J]. Journal of Dalian Maritime University. 2014, 40(1): 89-92

References

［1］SOLBERG A H S. Remote sensing of ocean oil-spill pollution［J］. Proceedings of the IEEE,2012, 100(10, SI): 2931-2945.

［2］BREKKE C, SOLBERG A H S. Oil spill detection by satellite remote sensing［J］. Remote Sensing Of Environment,2005,95: 1-13.

［3］童庆禧,张兵,郑兰芬. 高光谱遥感——原理、技术与应用［M］.北京:高等教育出版社,2006.

［4］SALEM F M F. Hyperspectral remote sensing a new approach for oil spill detection and analysis［D］. Virginia, United States: George Mason University, 2003.

［5］SANCHEZ G, ROPER W E, GOMEZ R. Detection and monitoring of oil spills using hyperspectral imagery［J］.Geo-Spatial and Temporal Images and Data Exploitation III, 2003(5097): 233-240.

［6］PLAZA J, PREZ R, PLAZA A, et al. Mapping oil spills on sea water using spectral mixture analysis of hyperspectral image data［J］.Chemical and Biological Standoff Detection III,2005(5995): 91-98.

［7］JOYE S B, MACDONALD I R, LEIFER I, et al. Magnitude and oxidation potential of hydrocarbon gases released from the BP oil well blowout［J］. Nature Geoscience,2011, 4(3): 160-164.

［8］SVEJKOVSKY J, LEHR W, MUSKAT J, et al. Operational utilization of aerial multispectral remote sensing during oil spill response: lessons learned during the deepwater horizon (MC-252) spill［J］. Photogrammetric Engineering and Remote Sensing, 2012, 78(10): 1089-1102.

［9］GREEN R O,EASTWOOD M L,SARTURE C M. Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS)［J］.Remote Sensing of Environment,1998(65): 227-248.

［10］BRADLEY E S, ROBERTS D A, DENNISON P E. Google earth and Google fusion tables in support of time-critical collaboration: mapping the deepwater horizon oil spill with the AVIRIS airborne spectrometer［J］. Earth Science Informatics,2011(4): 169-179.

［11］GREEN A A, BERMAN M, SWITZER P, et al. A transformation for ordering multispectral data in terms of image quality with implications for noise removal［J］. IEEE Transactions on Geoscience and Remote Sensing,1998, 26(1): 65-74.

［12］李海涛,顾海燕,张兵,等.基于MNF和SVM的高光谱遥感影像分类研究［J］.遥感信息,2007(5): 12-15. LI Hai-tao,GU Hai-yan,ZHANG Bing, et al. Research on hyperspectral remote sensing image classification based on MNF and SVM ［J］. Remote Sensing Information,2007(5):12-15.(in Chinese)