A Hybrid Multi-Scale Phase-Correlation Framework for Subpixel Registration of Multi-Temporal Very-High-Resolution Remote Sensing Images

This paper proposes a hybrid phase-correlation framework with a new multiscale detector, Fusion. Using a combination of FAST and Shi–Tomasi keypoints, followed by a probabilistic Hough transform and Canny edge detection, this detector improves repeatability. In addition, due to the limited ability of standard phase correlation to handle large geometric displacements, a complementary strategy is required to achieve sub-pixel matching precision. First, corners are extracted from both reference and sensed images using the Fusion detector. Corresponding points are then identified through coarse-to-fine phase correlation across a Gaussian pyramid. At each level, phase correlation yields an initial displacement, which is refined to sub-pixel accuracy using 1D parabolic fitting and propagated upward through the pyramid to obtain the final displacement. The proposed approach is evaluated using Pleiades and Sentinel-2 satellite images. Compared with the Scale-Invariant Feature Transform (SIFT)-based method and the detector-free Local Feature Transformer (LoFTR), the proposed framework achieves an RMSE below 0.2 and 0.4 pixels for Sentinel-2 and Pleiades imagery, respectively. Moreover, the results of the optimization analysis have revealed that shows that 2D paraboloid fitting combined achieves the lowest registration error of 0.010 pixels and the highest inlier ratio of 40.6%. The proposed approach achieves sub-pixel accuracy in the presence of noise and produces large numbers of correct matching points across different image resolutions.