Using Cross-sensor Image Learning for CBERS CCD Bands Simulation
YU Le1, CAO Kai2, WU Yang3, ZHANG Deng-rong4
1. Center for Earth System Science, Tsinghua University, Beijing 100084, China;
2. Center for Geographic Analysis, Harvard University, Cambridge MA02138, USA;
3. Department of Earth Sciences, Zhejiang University, Hangzhou 310027, China;
4. Institute of Remote Sensing and Geoscience, Hangzhou Normal University, Hangzhou 310026, China
The absence of two infrared bands (i.e. 1.55~1.75 μm (TM 5) and 2.08~2.35 μm (TM 7)) in CBERS CCD camera compared with Landsat TM/ETM+ results in a limitation that many algorithms developed for TM/ETM+ images are not applicable for CBERS CCD camera data directly. In this paper, a cross-sensor image learning approach is used to simulate new Landsat-like infrared bands so as to extend spectrum coverage for CBERS CCD camera data. A support vector regression (SVR) technique is used to model nonlinear relationship between a priori knowledge from ETM+ DN values and four CBERS CCD bands, and then new CBERS CCD bands are predicted. Experimental result shows good correlation between simulated band and corresponding ETM+ band.
[5] Tang J W,Gu X F,Niu S L,et al.Water Target Based Cross-calibration of CBERS-02 CCD Camera with MODIS Data [J].Science in China Series E:Engineering and Materials Science,2005,48:61-71.
[6] Li X,Gu X,Min X.Radiometric Cross-calibration of the CBERS-02 CCD Camera with the TERRA MODIS [J].Science in China Series E:Engineering and Materials Science,2005,48:40-60.
[8] 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 [J].International Journal of Remote Sensing,1985,6(1):75-88.
[9] Fraser S J,Green A A.A Software Defoliant for Geological Analysis of Band ratio [J].International Journal of Remote Sensing,1987,8(3):525-532.
[10] Rowan L C,Pawlewicz M J,Jones O D.Mapping Thermal Maturity in the Chainman Shale,Near Eureka,Nevada,with Landsat Thematic Mapper Images [J].American Association of Petroleum Geologists Bulletin,1992,76(7):1008-1023.
[12] Verhoef W,Bach H.Simulation of Hyperspectral and Directional Radiance Images Using Coupled Biophysical and Atmospheric Radiative Transfer Models [J].Remote Sensing of Environment,2003,87(1):23-41.
[16] Srivastava A N,Oza N C,Stroeve J.Virtual Sensors:Using Data Mining Techniques to Efficiently Estimate Remote Sensing Spectra [J].IEEE Transactions on Geoscience and Remote Sensing,2005,43(3):590-600.
[17] 边肇祺,张学工.模式识别 [M].2版.北京:清华大学出版社,2000:338.
[18] Burges C J.A Tutorial on Support Vector Machines for Pattern Recognition [J].Data Mining and Knowledge Discovery,1998,2(2):121-167.
[19] Duda R O,Hart P E,Stock D G.Pattern Classification [M].2nd ed.United States:John Wiley & Sons Inc,2001.
[20] Melgani F,Bruzzone L.Classification of Hyperspectral Remote Sensing Images with Support Vector Machines [J].IEEE Transactions on Geoscience and Remote Sensing,2004,42(8):1778-1790.
[21] Foody G M,Mathur A.A Relative Evaluation of Multiclass Image Classification by Support Vector Machines [J].IEEE Transactions on Geoscience and Remote Sensing,2004,42(6):1335-1343.
[22] Pal M,Mather P M.Support Vector Classification in Remote Sensing [J].International Journal of Remote Sensing,2005,26(5):1007-1011.
[23] Oommen T,Misra D,Twarakavi N K C,et al.An Objective Analysis of Support Vector Machine Based Classification for Remote Sensing [J].Mathematical Geosciences,2008,40(4):409-422.
2011, Vol. 23 
