| Technology and Methodology |
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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 |
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Abstract 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.
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Issue Date: 07 September 2011
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2011,
Vol. 23
