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REMOTE SENSING FOR LAND & RESOURCES    2014, Vol. 26 Issue (4) : 41-45     DOI: 10.6046/gtzyyg.2014.04.07
Technology and Methodology |
Water information extraction from remote sensing image using EMD and fraction method
ZHOU Lintao, YANG Guofan, ZHAO Fuqiang, DU Juan
College of Water Recourses, Shenyang Agriculture University, Shenyang 110866, China
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Abstract  

This paper presents a model for extracting water from remote sensing by using empirical mode decomposition(EMD)and fractal theory. The authors tried to improve accuracy with spectral information and texture characteristics. Principal component analysis was carried out on the image to obtain the biggest first principal component that contains effective information, then the fractal dimension of each pixel was calculated; at the same time, the first principal component was decomposed with the method of EMD to get the first three empirical mode functions, which, coupled with the original band information, served as the research data. With the method of maximum likelihood classifier, the waters were extracted. This method fully combines the advantages of EMD method in noise reduction and the advantage of fractal theory in texture information extraction. Experiment shows that this method can effectively improve the extraction accuracy, with the Kappa up to 0.932 5.

Keywords TerraSAR-X      polarimetric SAR      extraction of sea ice      object-oriented algorithm     
:  TP75  
Issue Date: 17 September 2014
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ZHAO Xinggang
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ZHAO Xinggang,LIU Lin,QIAN Jing. Water information extraction from remote sensing image using EMD and fraction method[J]. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(4): 41-45.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2014.04.07     OR     https://www.gtzyyg.com/EN/Y2014/V26/I4/41

[1] Barton I J,Bathols J M.Monitoring floods with AVHRR[J].Remote Sensing of Environment,1989,30(1):89-94.

[2] Steven M K,Patrick L,Brezonik L G,et al.A procedure for regional lake water clarity assessment using Landsat multispectral data[J].Remote Sensing of Environment,2002,82(1):38-47.

[3] 陈蕾,邓孺孺,陈启东,等.基于水质类型的TM图像水体信息提取[J].国土资源遥感,2012,24(1):90-94. Chen L,Deng R R,Chen Q D,et al.The extraction of water body information from TM imagery based on water quality types[J].Remote Sensing for Land and Resources,2012,24(1):90-94.

[4] 宋启帆,王少军,张志,等.基于WorldView II图像的钨矿区水体信息提取方法研究——以江西大余县为例[J].国土资源遥感,2011,23(2):33-36. Song Q F,Wang S J,Zhang Z,et al.A water information extraction method based on WorldView II Remote sensing image in tungsten ore districts:A case study of Dayu County in Jiangxi Province[J].Remote Sensing for Land and Resources,2011,23(2):33-36.

[5] 郭振亚,王心源,王传辉,等.巢湖流域水体信息提取方法研究[J].遥感技术与应用,2012,27(3):443-448. Guo Z Y,Wang X Y,Wang C H,et al.The research on extraction method of water body information in Chaohu lake basin[J].Remote Sensing Technology and Application,2012,27(3):443-448.

[6] Argialas D,Tzotsos A.Automatic extraction of physiographic features and alluvial fans in Nevada,USA from digital elevation models and satellite imagery through multiresolution segmentation and object-oriented classification[C]//Proceedings of Asprs 2006 Annual Conference,2006.

[7] 都金康,黄永胜,冯学智,等.SPOT卫星影像的水体提取方法及分类研究[J].遥感学报,2001,5(3):214-218. Du J K,Huang Y S,Feng X Z,et al.Study on water bodies extraction and classification from SPOT image[J].Journal of Remote Sensing,2001,5(3):214-218.

[8] Zheng Q N,Laironald J,Huang N E,et al.Observation of ocean current response to 1998 hurricane georges in the Gulf of Mexico[J].Acta Oceanologica Sinica,2006,25(1):1-9.

[9] 杨玉静,冯建辉.纹理特征提取及辅助遥感影像分类技术研究[J].海洋测绘,2008,28(4):37-40. Yang Y J,Feng J H.Research on extraction and assistant classification of remote sensing for texture feature[J].Hydrographic Survey and Charting,2008,28(4):37-40.

[10] Liu Z S,Wu S H,Li H,et al.Operational observations of three dimensional wind with incoherent Doppler wind LiDAR[C]//Proc of 25th International Laser Radar Conference,2010.

[11] 张毅坤,麻晓畅,华灯鑫,等.基于EMD-DISPO的Mie散射激光雷达回波信号去噪方法研究[J].光谱学与光谱分析,2011,31(11):2996-3000. Zhang Y K,Ma X C,Hua D X,et al.The Mie scattering LiDAR return signal denoising research based on EMD-DISPO[J].Spectroscopy and Spectral Analysis,2011,31(11):2996-3000.

[12] Yin S R,Wang W R.LiDAR signal denoising based on wavelet domain spatial filtering[C]//2006 CIE International Conference on Radar.Shanghai,China:Institute of Electrical and Electronics Engineers Inc,2007:1-3.

[13] 李卿,张国平,刘洋.基于EMD的拉曼光谱去噪方法研究[J].光谱学与光谱分析,2009,29(1):142-144. Li Q,Zhang G P,Liu Y.A study of Raman spectra denoising based on empirical mode decomposition[J].Spectroscopy and Spectral Analysis,2009,29(1):142-144.

[14] 朱骥,林子瑜,王昂生.数字图像单个像元分形维数的特征与计算方法[J].光电工程,2005,32(2):24-27. Zhu J,Lin Z Y,Wang A S.Features and algorithm of fractal dimension of single pixel in digital image[J].Opto-Electronic Engineering,2005,32(2):24-27.

[15] 王娟,张军,吕兆峰.基于分形纹理的遥感影像土地覆盖的分类方法研究[J].测绘科学,2008,33(2):15-17,32. Wang J,Zhang J,Lü Z F.Study on classification of land cover with remote sensing image based on fractal texture[J].Science of Surveying and Mapping,2008,33(2):15-17,32.

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