SAR Image Registration with Sift Features and Edge Points

When registering synthetic aperture radar (SAR) images with inferior quality, general methods based on a single feature usually suffer from feature scarcity. To address the problem, a multi-feature registration algorithm is proposed in this paper. This algorithm utilizes scale-invariant feature transform (SIFT) feature points and obvious edge points as the keypoints of SAR images. Meanwhile, the ratio of exponentially weighted average (ROEWA) is adopted to calculate image gradient and extract edge features. Then, it employs gradient location-orientation histograms (GLOH) to construct feature vectors and matches keypoints based on Euclidean distance. Experiments on SAR image registration demonstrate that the proposed registration algorithm can provide more matching feature points and improve the registration accuracy compared with SIFT, SAR-SIFT and SIFT-Harris algorithms.