全部 标题 作者
关键词 摘要

OALib Journal期刊
ISSN: 2333-9721
费用:99美元

查看量下载量

相关文章

更多...

基于影像交叉学习的CBERSCCD波段模拟

DOI: 10.6046/gtzyyg.2011.03.09, PP. 48-53

Keywords: 波段模拟,机器学习,SVR,CBERS,CCD,TM/ETM+

Full-Text   Cite this paper   Add to My Lib

Abstract:

与TM/ETM+相比,CBERSCCD缺少2个红外波段(波段5和波段7),这便导致了许多针对TM/ETM+数据的图像处理方法难以直接应用于CBERSCCD图像。为此,采用基于影像交叉学习的波段模拟方法,即以ETM+数据作为先验知识,通过支持向量回归(SupportVectorRegression,SVR),拟合CBERSCCD与ETM+7波段DN值之间的非线性关系,进而在CBERSCCD已有波段的基础上模拟一个新的波段图像。实验结果表明,采用该方法模拟的CBERSCCD新波段与验证波段之间具有较高的相关性。

References

[1]  Qiao Y L,Zhao S M,Zhen L,et al.Application of China-Brazil Earth Resources Satellite in China
[2]  [J] Advances in Space Research,2009,43(6):917-922.
[3]  张振生.CBERS-02数据在土地沙化、矿山环境、土地利用监测中的应用
[4]  [J] CBERS应用简讯,2006(3):10-11.
[5]  中国资源卫星应用中心
[6]  [EB/OL]
[7]  [2010-06-
[8]  彭光雄,何宇华,李京,等.中巴地球资源卫星02星CCD图像交叉定标与大气校正研究
[9]  [J] CBERS应用简讯,2007(3):14-15.
[10]  Tang J W,Gu X F,Niu S L,et al.Water Target Based Cross-calibration of CBERS-02 CCD Camera with MODIS Data
[11]  [J] Science in China Series E:Engineering and Materials Science,2005,48:61-71.
[12]  Li X,Gu X,Min X.Radiometric Cross-calibration of the CBERS-02 CCD Camera with the TERRA MODIS
[13]  [J] Science in China Series E:Engineering and Materials Science,2005,48:40-60.
[14]  国土资源部02B星数据应用与推广办公室.CBERS-02B星数据国土资源应用评价
[15]  [J] CBERS应用简讯,2008(1):14.
[16]  Elvidge C D,Lyon R J P.Estimate of the Vegetation Contribution to the 1.65/2.22 μm Ratio in Airborne Thematic-mapper Imagery of the Virginia Range,Nevada
[17]  [J] International Journal of Remote Sensing,1985,6(1):75-88.
[18]  Fraser S J,Green A A.A Software Defoliant for Geological Analysis of Band ratio
[19]  [J] International Journal of Remote Sensing,1987,8(3):525-532.
[20]  Rowan L C,Pawlewicz M J,Jones O D.Mapping Thermal Maturity in the Chainman Shale,Near Eureka,Nevada,with Landsat Thematic Mapper Images
[21]  [J] American Association of Petroleum Geologists Bulletin,1992,76(7):1008-1023.
[22]  陈方,牛铮,覃驭楚,等.基于宽光谱光学遥感图像的细分光谱光学遥感图像的模拟
[23]  [J] 光电工程,2007,34(5):89-96.
[24]  Verhoef W,Bach H.Simulation of Hyperspectral and Directional Radiance Images Using Coupled Biophysical and Atmospheric Radiative Transfer Models
[25]  [J] Remote Sensing of Environment,2003,87(1):23-41.
[26]  苏里宏,李小文,梁顺林,等.典型地物波谱库的数据体系与波谱模拟
[27]  [J] 地球信息科学,2006,4(4):7-15.
[28]  程熙,沈占锋,骆剑承,等.利用地物波谱学习的遥感影像波段模拟方法
[29]  [J] 红外与毫米波学报,2010,29(1):45-48,62.
[30]  叶泽田,顾行发.利用MIVIS数据进行遥感图像模拟的研究
[31]  [J] 测绘学报,2000,29(3):235-239.
[32]  Srivastava A N,Oza N C,Stroeve J.Virtual Sensors:Using Data Mining Techniques to Efficiently Estimate Remote Sensing Spectra
[33]  [J] IEEE Transactions on Geoscience and Remote Sensing,2005,43(3):590-600.
[34]  边肇祺,张学工.模式识别
[35]  [M] 2版.北京:清华大学出版社,2000:338.
[36]  Burges C J.A Tutorial on Support Vector Machines for Pattern Recognition
[37]  [J] Data Mining and Knowledge Discovery,1998,2(2):121-167.
[38]  Duda R O,Hart P E,Stock D G.Pattern Classification
[39]  [M] 2nd ed.United States:John Wiley & Sons Inc,2001.
[40]  Melgani F,Bruzzone L.Classification of Hyperspectral Remote Sensing Images with Support Vector Machines
[41]  [J] IEEE Transactions on Geoscience and Remote Sensing,2004,42(8):1778-1790.
[42]  Foody G M,Mathur A.A Relative Evaluation of Multiclass Image Classification by Support Vector Machines
[43]  [J] IEEE Transactions on Geoscience and Remote Sensing,2004,42(6):1335-1343.
[44]  Pal M,Mather P M.Support Vector Classification in Remote Sensing
[45]  [J] International Journal of Remote Sensing,2005,26(5):1007-1011.
[46]  Oommen T,Misra D,Twarakavi N K C,et al.An Objective Analysis of Support Vector Machine Based Classification for Remote Sensing
[47]  [J] Mathematical Geosciences,2008,40(4):409-422.

Full-Text

Contact Us

service@oalib.com

QQ:3279437679

WhatsApp +8615387084133