
Proceedings Paper
Research of parameters solution method of Rational Function ModelFormat | Member Price | Non-Member Price |
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Paper Abstract
As the sensor technique develops rapidly, especially the high-precision commercial satellite, Rational Function Model
(RFM) has received increasingly more acceptance because of its simple form, convenience for using, low requirement
for specialized knowledge and not depending on the imaging parameters of specific satellite. Nevertheless, the quality of
applying the model depends on the precision of parameters. Because of the ill-conditioned equation during solving the
RFM, traditional Least square (LS) method can not always offer high-precision solution, therefore, it is essential to
research better methods to provide theory basis for wider application of RFM. In the paper, truncation singular value
decomposition, Genetic Algorithm and various biased estimation methods are applied to solve the parameters of RFM,
and detailed analysis is made about the results of different algorithms that they all can solve the ill-conditioned equation
well and improve the precision. The conclusion is that truncation singular value decomposition, Genetic Algorithm and
using biased estimation method reasonably can lead to high-precision parameters but Genetic Algorithm remains to be
improved due to its extensive time.
Paper Details
Date Published: 14 November 2007
PDF: 7 pages
Proc. SPIE 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, 67901R (14 November 2007); doi: 10.1117/12.749318
Published in SPIE Proceedings Vol. 6790:
MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications
Yongji Wang; Jun Li; Bangjun Lei; Chao Wang; Liang-Pei Zhang; Jing-Yu Yang, Editor(s)
PDF: 7 pages
Proc. SPIE 6790, MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications, 67901R (14 November 2007); doi: 10.1117/12.749318
Show Author Affiliations
Bin Jia, Remote Sensing Satellite Ground Station (China)
Graduate School of the Chinese Academy of Sciences (China)
Huihui Xie, Remote Sensing Satellite Ground Station (China)
Graduate School of the Chinese Academy of Sciences (China)
Graduate School of the Chinese Academy of Sciences (China)
Huihui Xie, Remote Sensing Satellite Ground Station (China)
Graduate School of the Chinese Academy of Sciences (China)
Jian Hu, Academy of Opto-Electronics (China)
Published in SPIE Proceedings Vol. 6790:
MIPPR 2007: Remote Sensing and GIS Data Processing and Applications; and Innovative Multispectral Technology and Applications
Yongji Wang; Jun Li; Bangjun Lei; Chao Wang; Liang-Pei Zhang; Jing-Yu Yang, Editor(s)
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