Comparison research of algorithms about ortho-rectification for remote sensing image

There are kinds of methods for ortho-rectification in application of remote sensing, including Collinearity Equation Model, Strict Geometric Model based on Affine Transformation, Improved Polynomial Model, Rational Function Model, Method based on Neural Network, and so on. But there is lack of system comparison between these methods. On the basis of detailing the algorithm of these methods above, advantages and drawbacks about these algorithms are summarized in this paper. Specific emphasis is the mathematical derivation and algorithm of RFM. Two kinds of algorithm based on neural network were taken in application of ortho-rectification. To compare accuracy and effective between the above methods, we also detailed the processing steps and make some experiments. The result shows that: in the condition of the same GCPs distribution, Rational Function Model that can reach sub pixel accuracy is the best of all from the viewpoint of precision, which can be used in practice in spite of its relatively slower speed.