期刊文献+

基于机载高光谱遥感数据的溢油信息提取方法 被引量:6

Extraction method of oil spill information using airborne hyper-spectral remote sensing data
原文传递
导出
摘要 为减少高光谱遥感数据处理波段,提高处理效率,提出一种基于最小噪声分离波谱的决策树分类方法.利用最小噪声分离变换(MNF)降低数据冗余度,并将图像噪声分离,通过分析各地物的MNF特征值,建立分类决策树,并提取了油膜相对厚度等信息.结果表明,该方法保证了识别精度,有效利用了光谱维信息,明显减小了数据处理时间,从而将高光谱数据用于溢油应急快速产品的生产中. 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.
出处 《大连海事大学学报》 CAS CSCD 北大核心 2014年第1期89-92,共4页 Journal of Dalian Maritime University
基金 国家自然科学基金资助项目(41071260) 中国石油天然气股份有限公司科学研究与技术开发资助项目(2011A-0209-01) 中央高校基本科研业务费专项资金资助(3132014023)
关键词 高光谱遥感 溢油监测 最小噪声分离 hyper-spectral remote sensing oil spill monitoring minimum noise fraction(MNF)
  • 相关文献

参考文献11

  • 1SOLBERG A H S. Remote sensing of ocean oil - spill pollution[J]. Proceedings of the IEEE,2012, 100(10, SI) : 2931 -2945.
  • 2BREKKE C, SOLBERG A H S. Oil spill detection by satellite remote sensing [ J ]. Remote Sensing Of Environ- ment,2005,95 : 1 - 13.
  • 3SALEM F M F. Hyperspectral remote sensing a new ap- proach for oil spill detection and analysis [ D ]. Virginia, United States: George Mason University, 2003.
  • 4SANCHEZ 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.
  • 5PLAZA J, PIREZ R, PLAZA A, et al. Mapping oil spills on sea water using spectral mixture analysis of hy- perspectral image data [ J ]. Chemical and Biological Standoff Detection II1,2005 (5995) : 91 - 98.
  • 6JOYE S B, MACDONALD I R, LEIFER I, et al. Magni- tude and oxidation potential of hydrocarbon gases released from the BP oil well blowout [ J ]. Nature Geoscience, 2011,4(3) : 160 -164.
  • 7SVEJKOVSKY J, LEHR W, MUSKAT J, et al. Opera- tional utilization of aerial multispectral remote sensing during oil spill response: lessons learned during the deepwater horizon ( MC - 252) spill[ J ]. Photogrammet- ric Engineering and Remote Sensing, 2012, 78 ( 10 ) : 1089 - 1102.
  • 8GREEN R O, EASTWOOD M L, SARTURE C M. Ima- ging spectroscopy and the airborne visible/infrared ima- ging spectrometer (AVIRIS) [J ]. Remote Sensing of En- vironment, 1998 (65) : 227 - 248.
  • 9BRADLEY 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.
  • 10GREEN A A, BERMAN M, SWITZER P, et al. A transformation for ordering muhispectral data in terms of image quality with implications for noise removal [ J ]. IEEE Transactions on Geoscience and Remote Sensing, 1998, 26(1) : 65 -74.

二级参考文献7

  • 1惠文华.基于支持向量机的遥感图像分类方法[J].地球科学与环境学报,2006,28(2):93-95. 被引量:46
  • 2Vladimir N Vapnik 张学工译.统计学习理论的本质[M].北京:清华大学出版社,2000..
  • 3Green A A etc. A transformation for ordering multispectral data in terms of image quality with implications for noise removal [J]. IEEE Transactions on Geoscience and Remote Sensing, 1988, 26(1): 65-74.
  • 4Gualtieri J A, Cromp R F. Support vector machines for hyperspectral remote sensing classification[A]. The 27th AIPR Workshop, Advances in Computer Assisted Recognition [C]. Washington D C,1998.
  • 5Zhu G B, and Blumberg D G. Classification using ASTER data and SVM algorithms: The case Study of beer sheva, israel [J].Remote Sensing of Environment, 2002,80(2) : 233-240.
  • 6Burges C J C. A tutorial on support vector machines for pe,tern recognition[J]. Knowledge Discovery and Data Mining, 1998, 2(2): 121-167.
  • 7张学工.关于统计学习理论与支持向量机[J].自动化学报,2000,26(1):32-42. 被引量:2312

共引文献30

同被引文献60

  • 1樊凤杰,轩凤来,白洋,纪会芳.基于LLE-RF的中药三维荧光光谱分类识别[J].计量学报,2020,41(2):263-268. 被引量:8
  • 2赵云升,吴太夏,罗杨洁,赵丽丽,周启超.水面溢油的多角度偏振与二向性反射定量关系研究[J].遥感学报,2006,10(3):294-298. 被引量:18
  • 3FINGAS M, BROWN C E. Oil spill remote sensing: A review [ M ]// Oil spill science and technology. New York : Gulf Profes- sional Publishing ,2011 : 111-169.
  • 4BREKKE C, SOLBERG A H S. Oil spill detection by satellite re- mote sensing[ J]. Remote Sensing of Environment,2005,95 ( 1 ) : 1-13.
  • 5DE BEUKELAER S M, MACDONALD I R, GUINNASSO N L JR, et al. Distinct side-scan sonar, RADARSAT SAR, and acous- tic profiler signatures of gas and oil seeps on the Gulf of Mexico slope[ J]. Geo-Marine Letters,2003,23 (3/4) : 177-186.
  • 6WETTLE M, DANIEL P J, LOGAN G A, et al. Offshore petroleumexploration from space: A developing capability at Geoscience Australia [ C ]/! Proceedings of OCEANS 2010. Sydney, NSW : IEEE ,2010 : 1-7.
  • 7LAMMOGLIA T,DE SOUZA FILHO C R. Spectroscopic charac- terization of oils yielded from Brazilian offshore basins : Potential applications of remote sensing [ J ]. Remote Sensing of Environ- ment ,2011,115 (10) :2525-2535.
  • 8LEIFER I,LEHR W J, SIMECEK-BEATTY D, et al. State of the art satellite and airborne marine oil spill remote sensing:Appli- cation to the BP Deepwater Horizon oil spill [ J 1. Remote Sensing of Environment, 2012, 124: 185-209.
  • 9CLARK R N, SWAYZE G A, LEIFEB. I, et al. A method for quantitative map- ping of thick oil spills using imaging spectroscopy [ R ]. Virgini- a: U. S. Geological Survey,2010 : 1-7.
  • 10LAMMOGLIA T, DE SOUZA FILHO C R. Mapping and charac- terization of the API gravity of offshore hydrocarbon seepages u- sing multispectral ASTER data [ J ]. Remote Sensing of Environ- ment,2012,123:381-389.

引证文献6

二级引证文献19

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部