Optical and synthetic aperture radar (SAR) image registration (OSIR) based on structural feature points is essential for all-weather and all-day multi-source data alignment. However, such methods are often plagued by insufficient correct correspondences caused by speckle noise and textural similarity, by which OSIR accuracy is limited. Therefore, a high-precision framework combining structure-confidence-weighted phase congruency (PC) with window-scaled cascaded (WSC) matching is proposed in the paper, through which accuracy is enhanced via the synergistic reinforcement of feature detection and matching. In feature detection, a weighting factor based on relative total variation and nonsubsampled contourlet transform is designed for PC calculation (i.e., RNW-PC). By suppressing noise-induced pseudo-structures and selectively enhancing cross-modal consistent features, the repeatability of keypoints is significantly improved. In feature matching, a coarse-to-fine WSC approach is proposed. A scaling window and cosine similarity are introduced, by which a refined directional consistency screening of candidate points within the neighborhood is performed based on the spatial constraints established during the coarse matching phase. Omissions in correct correspondences are effectively reduced through dual constraints of distance and direction. On 60 image pairs, the proposed method outperforms existing point-based algorithms, with NCM increasing by at least 2.43-fold and accuracy improving by over 19.85%.
Lian et al. (Fri,) studied this question.