Optical and SAR Image Registration Based on Multi-Scale Orientated Map of Phase Congruency

Abstract

Optical and Synthetic Aperture Radar (SAR) images are highly complementary, and their registrations are a fundamental task for other remote sensing applications. Traditional feature-matching algorithms fail to solve the significant nonlinear radiation difference (NRD) caused by different sensors. To address this problem, a robust registration algorithm with the multi-scale orientated map of phase congruency (MSPCO) is proposed. First, a nonlinear diffusion scale space is established to obtain the scale invariance of feature points. Compared with the linear Gaussian scale space, the nonlinear diffusion scale space can better preserve the edge and texture information. Second, to ensure the quantity and repeatability of features, corner points and edge points are detected on the moment map of phase congruency, respectively, which is the foundation to the next feature matching. Third, the MSPCO descriptor is constructed via the orientation of phase congruency (PCO). PCO is highly robust to NRD, and the different scales of PCOs enhance the robustness of the descriptor. Finally, a feature-matching strategy based on an effective scale ratio is proposed, which reduces the number of comparisons among features and improves computational efficiency. The experimental results show that the proposed method is better than the existing feature-based methods in terms of the number of correct matches and registration accuracy. The registration accuracy is only inferior to that of the most advanced template matching method, and the accuracy difference is within 0.3 pixels, which fully demonstrates the robustness and accuracy of the proposed method in optical and SAR image registration.

Optical and SAR Image Registration Based on the Phase Congruency Framework

“…However, it cannot solve large-scale and rotation differences. Meanwhile, Jia et al [16] used the novel nonlinear diffusion scale space to obtain the keypoints, and it improved the computational efficiency by optimizing the number of matching correspondences.…”

Generalized Quantification Function of Monogenic Phase Congruency

“…Phase congruency is another technique used for edge detection, although it is less well-known. Despite several recent works utilizing this method, such as its integration into pipelines for image registration or edge detection in various applications [11][12][13], there have been no new developments concerning the principles of this technique, except those produced by the authors themselves [14].…”