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Proceedings Paper

A ground control points sampling design method based on smallest singular value
Author(s): Pu Wang; Wei An; Xing Pu Deng; Jing Guo; Jun Li
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Paper Abstract

The Ground Control Points (GCPs) are widely used in geometric correction for remote sensing imagery, and the distribution of them is a key factor which affects the accuracy and quality of image correction. In this paper, we propose a new sampling design method, called Smallest Singular Value-based Sampling (SSVS), to obtain the optimal distribution of the GCPs. When the geometric correction of remote sensing imagery is performed with a 2D or 3D polynomial function model, the estimation of geometric correction model parameters can be interpreted as an estimation of regression coefficients with a Multiple Linear Regression(MLR) model, whose design matrix depends on the coordinates of GCPs. From the perspective of regression model, the design matrix of MLR should be optimized to obtain the most accurate regression coefficients. In this paper, it has been proved that the Smallest Singular Value(SSV) of design matrix is inversely proportional to the upper bound of estimation errors. By choosing the optimal distribution of GCPs, the SSV of design matrix can be maximized and the upper bound of estimation errors can be minimized. Therefore, the SSV of design matrix is used as a criterion, and the objective of SSVS is to find the sample pattern that has the biggest SSV. In this paper, the simulation annealing is employed to search the optimal pattern. Two experiments were carried out to test SSVS. The results indicate that the SSVS is an effective GCPs sampling design method and can be applied to evaluate upper bound of estimation error.

Paper Details

Date Published: 15 October 2014
PDF: 10 pages
Proc. SPIE 9244, Image and Signal Processing for Remote Sensing XX, 92441E (15 October 2014); doi: 10.1117/12.2065796
Show Author Affiliations
Pu Wang, National Univ. of Defense Technology (China)
Wei An, National Univ. of Defense Technology (China)
Xing Pu Deng, National Univ. of Defense Technology (China)
Jing Guo, National Univ. of Defense Technology (China)
Jun Li, National Univ. of Defense Technology (China)


Published in SPIE Proceedings Vol. 9244:
Image and Signal Processing for Remote Sensing XX
Lorenzo Bruzzone, Editor(s)

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