Automatic co-registration performance of fully polarimetric SAR images with texture relationship

This paper presents a methodology for automatic co registration of polarimetric Synthetic Aperture Radar (SAR) images obtained from similar satellites, but with differing view angles. The Scale Invariant Feature Transform (SIFT) algorithm is applied to extract candidate Ground Control Points (GCPs), where two-way keypoint matching eliminates improbable correspondence keypoints. Minimizing the Root Mean Squared Error (RMSE) also removes matching points with large RMSE and refines the location of points in a warp image through a pseudo-affine transformation. In addition, information entropy and spatial dispersion quality constraints allow quantification of the spatial distribution of the GCPs. In terms of the polarimetric SAR registration, co registration using total power (TP) images can be used to minimize the total registration error for all of the polarization, as the TP combination generates the smallest total RMSE. In addition, geometric calibration can be used to improve the accuracy of co-registration. Moreover, the influence of differences in the texture patterns of polarimetric views on the co-registration performance is investigated.