DocumentCode :
2841212
Title :
A BP Training Fitting Method about Multivariate BRDF Based on B-spline Function
Author :
Jun Yu ; Weina Tu ; Zhan Wang
Author_Institution :
Dept. of Appl. Math., Nanjing Univ. of Sci. & Technol., Nanjing, China
fYear :
2012
fDate :
24-25 July 2012
Firstpage :
30
Lastpage :
32
Abstract :
For a large number of experimental data, the BRDF surface fitting method based on B-spline function and least squares theory, the ill-conditioned normal equations, the low accuracy of the results and long CPU time may be appeared. Thereby, in this paper by using the BP learning method, combined with the training process of L-M algorithm, an improved method is presented. And the method is applied to the BDRF data processing, the result shows that this method is effective, and has greatly improved in accuracy and reduced running time.
Keywords :
backpropagation; data handling; geophysics computing; least squares approximations; neural nets; remote sensing; splines (mathematics); surface fitting; B-spline function; BDRF data processing; BP learning method; BP training fitting method; BRDF surface fitting method; L-M algorithm; Levenberg-Marquardt algorithm; backpropagation; bidirectional reflectance distribution functions; least squares theory; multivariate BRDF; remote sensing application; Accuracy; Algorithm design and analysis; Fitting; Mathematical model; Neural networks; Splines (mathematics); Surface fitting; B spline function; BP algorithm; numerical analysis; surface fitting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information and Computing Science (ICIC), 2012 Fifth International Conference on
Conference_Location :
Liverpool
ISSN :
2160-7443
Print_ISBN :
978-1-4673-1985-0
Type :
conf
DOI :
10.1109/ICIC.2012.2
Filename :
6258063
Link To Document :
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