DTM modeling from remote sensing image with high resolution

Jianqing Zhang; Shunyi Zheng; Yong Zhang

doi:10.1117/12.540047

25 September 2003 DTM modeling from remote sensing image with high resolution

Jianqing Zhang, Shunyi Zheng, Yong Zhang

Proceedings Volume 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition; (2003) https://doi.org/10.1117/12.540047
Event: Third International Symposium on Multispectral Image Processing and Pattern Recognition, 2003, Beijing, China

Abstract

Two key important techniques of DTM (Digital Terrain Model) modelling from remote sensing image pair with high resolution (RSIPHR) are introduced in this paper. First is determining conjugate point pair automatically by image matching. Because the approximate epipolar image pair of some RSIPHR remains quite large y-parallelx after relative registration, 2 D relaxation matching sh ould be used in the DTM Modeling. The procedure includes feature point extraction, approximate value estimation, matching and refining, check and filter. Second is the calculation of the spatial coordinates for the conjugate point pair from matching. For IKONS and QUICKBIRD images, it can be based on the RPC/RPB (Rational Polynomial Coefficients) parameters. Because the coordinate accuracy, computed by RPC/RPB parameters, is quite lower in many cases, a block adjustment with an affine transform of image coordinates should be completed based on some control points. Then, the affine transform and RPC/RPB parameters should be used in computation. If there are control points more than 4, the new, simple and strict geometric model based on affine transformation could be applied for any RSIPHR. Only 8 affine coefficients and one slantwise angle need to be determined by control points. Then, they can be applied in the computation.

Citation Download Citation

Jianqing Zhang, Shunyi Zheng, and Yong Zhang "DTM modeling from remote sensing image with high resolution", Proc. SPIE 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition, (25 September 2003); https://doi.org/10.1117/12.540047

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