A Multilevel Point-Matching Algorithm Based on Hierarchical Feature Detection and Description for SAR-to-Optical Image Registration

• Published in: IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
• Main Authors: Zhixin Lian, Shiyang Tang, Jiahao Han, Yue Wu, Mingjin Zhang, Zhanye Chen, Linrang Zhang
• Format: Article
• Language: English
• Published: IEEE 2025-01-01
• Subjects: Feature description; feature detection; image registration; phase congruency (PC); synthetic aperture radar (SAR) to optical
• Online Access: https://ieeexplore.ieee.org/document/10906637/
• ISSN: 1939-1404; 2151-1535

High-precision registration of synthetic aperture radar (SAR) and optical images based on point features remains a particularly challenging task, as the detection and description of feature points are susceptible to nonlinear radiometric distortions and SAR speckle noise. For this purpose, a multilevel point-matching algorithm based on hierarchical feature detection and description is proposed in this letter to improve the accuracy of SAR-to-optical (S-O) image registration. First, a FAST feature detector (OIPC-Fast) is constructed by combining overlapping chunking, image stratification, and phase congruency (PC). The OIPC-Fast detector performs hierarchical feature detection on SAR and optical images based on image properties by two-dimensional discrete wavelet transform and multimoment of PC map, respectively. Feature points with high consistency are screened out by voting criteria. The repeatability of keypoints is effectively improved. Then, a multilevel matching strategy is proposed. The SAR feature descriptor is constructed in this strategy by capturing more layers of image information rather than using a single denoised SAR image information after preprocessing, thus enhancing the robustness of SAR feature descriptors. Ten sets of real image data are used for experimental validation. Compared with some of the most advanced algorithms, the results indicate that the registration accuracy can be improved by applying the proposed point-matching algorithm to S-O image registration.