Prominent Structure-Guided Feature Representation for SAR and Optical Image Registration
Ning Lv, Zhen Han, Hongxi Zhou, Chen Chen, Shaohua Wan, Tao Su · IEEE Geoscience and Remote Sensing Letters · 2024
Common feature representation in optical and synthetic aperture radar (SAR) image registration is one of the most challenging tasks due to the significant geometric and radiometric differences. This letter proposed aProminent structure-guided feature (PSGF)representation for SAR and optical image registration. Firstly, the prominent structure of the image is highlighted based on windowed inherent variations, which is conducive to identifying more accurate and reliable corresponding points. Secondly, the maximum response index filter banks are proposed to extract structure features with multi-orientation convolution results. Then the structure feature-guided representation generated from this filtering map is quantized in histograms. Finally, the descriptor with radiation invariance is employed for feature matching, enabling automatic image registration with high accuracy. Comparative analysis with state-of-the-art methods on diverse terrain data demonstrates the superiority of the proposed PSGF method for SAR and optical image registration.