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With increasing spatial resolution, the differences in nonlinear radiation and geometric properties between optical and synthetic aperture radar (SAR) images will be magnified, which will lead to lower registration accuracy. This paper addresses this problem and proposes an automatic registration method for HR optical and SAR images using the uniform optimized feature and the extended phase congruency (EPC) model. First, we combine two different gradient operators and a quadtree algorithm to improve the detection performance of the multi-scale Harris detector, especially for the repeatability and uniformity of feature points in optical and SAR images with large nonlinear radiation and severe speckle noise. Next, we use the EPC model with illumination and contrast invariance to generate the multi-scale orientation index map (MS-OIM) and the multi-orientation phase congruency amplitude map (MO-PCAM) and then build a novel GLOH-like local descriptor to obtain corresponding features based on the two feature maps above. Finally, the fast sample consensus (FSC) method is utilized to remove false matches and calculate the transformation parameters of sensed image based on the piecewise linear model. The detection results on synthetic data show that the proposed detector has better repeatability and uniformity than the other three classical detectors. The registration results obtained on real data indicate that our method is effective for optical and SAR images with speckle noise interference and nonlinear radiometric differences and achieves high registration accuracy.

HR optical and SAR image registration using uniform optimized feature and extend phase congruency