SAR simulation imaging may be useful for several purposes, and this paper focuses on the use of SAR image simulation
for accuracy analysis of image aid navigation in natural scene. However, natural scenes are more complicated including
randomly and anisotropic distributed. So the anisotropic fBm interpolation mold was proposed via improving fBm
method for obtaining a more accurate matrix of the DEM data. On the basis of high-resolution DEM data, an approach
was presented to SAR imaging simulation for comprehensive scenarios, which takes account scattering, shadowing and
the layover. In the simulation experiment, this algorithm was successfully implemented on producing SAR simulation
imaging, and can satisfy the requirements of accuracy analysis in SAR aid navigation system.
In this paper, a new multiscale kernel function model is proposed, according to kernel function nature and the wavelet
frame theory. Thus a new image support feature is defined based on the multiscale wavelet kernel regression for the
purpose of improving image matching algorithm. The comprehensive the feature match and grey correlation method, a
novel image fast matching algorithm is proposed based on support feature points. The partial Hausdorff distance is
adopted as a similarity measure combined with the wavelet decomposition iteration to search the fine strategy, in the
wavelet domain. According to support feature points the position in original image, extracts corresponding the grey
value, and achieves the correlation matching. The matched data are compressed effectively because support feature
points are sparse. Experimental results demonstrate that the proposed algorithm is robust, fast and can achieve matching
accurately.
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