Abstract
Over-parameterization and over-correction are two of the major problems in the rational function model (RFM). A new approach of optimized RFM (ORFM) is proposed in this paper. By synthesizing stepwise selection, orthogonal distance regression, and residual systematic error correction model, the proposed ORFM can solve the ill-posed problem and over-correction problem caused by constant term. The least square, orthogonal distance, and the ORFM are evaluated with control and check grids generated from satellite observation Terre (SPOT-5) high-resolution satellite data. Experimental results show that the accuracy of the proposed ORFM, with 37 essential RFM parameters, is more accurate than the other two methods, which contain 78 parameters, in cross-track and along-track plane. Moreover, the over-parameterization and over-correction problems have been efficiently alleviated by the proposed ORFM, so the stability of the estimated RFM parameters and its accuracy have been significantly improved.
Highlights
High-solution satellite imagery has been used widely in photogrammetry and remote sensing applications
rational function model (RFM) has its own disadvantages in accuracy: 1) over-parameterization: the 80 RPCs of RFM are usually strongly correlated, and the estimation of RPCs is an ill-posed problem, which should contribute to over-parameterization error in geometric rectification; 2) overcorrection: when all the measurement error considered, the constant term will be viewed as erroneous in coefficient matrix in RFM, the consequence will be usually inaccurate because of the effects of measurement error exaggerated
A new parameter optimized method of RFM based on stepwise selection, orthogonal regression, and residual systematic error correction model, is proposed in this paper
Summary
High-solution satellite imagery has been used widely in photogrammetry and remote sensing applications. Such as natural resources monitoring, stereo mapping, and orthophotography generation(Jacobsen 2004). The rational function model (RFM) is generic(Tao and Hu 2001), i.e., its model parameters do not carry physical meanings of the imaging process. The ill-posed problems in RPCs can be addressed through the least square method and ridge estimation. The automatic determination of the optimal regularization parameter of ridge estimation is very complex to obtain, and the overcorrection has never been considered in RFM. A new parameter optimized method of RFM based on stepwise selection, orthogonal regression, and residual systematic error correction model, is proposed in this paper.
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