SAR Image Registration Using an Improved SAR-SIFT Algorithm and Delaunay-Triangulation-Based Local Matching

Abstract

Synthetic aperture radar (SAR) image registration is a challenging task in remote sensing because of the presence of significant intensity as well as geometric differences between the images. Moreover, the influence of multiplicative speckle noise in SAR images adversely affects the registration result. The synthetic aperture radar-scale invariant feature transform (SAR-SIFT) algorithm, specially developed for the SAR images, has been used popularly for the automatic registration of SAR images based on global feature matching. However, the algorithm suffers from lack of controllability of the number of extracted features and uneven distribution of the features. Moreover, global matching of the features reduces the number of correct matches. To address these problems, we propose an improved SAR-SIFT (I-SAR-SIFT) algorithm, which extracts features from the SAR images by considering three important factors of feature-based image registration: stability, distinctiveness, and distribution. Moreover, a novel approach is adapted to improve the distinctiveness of the feature–descriptor in the presence of speckle noise. In addition, a Delaunay-triangulation-based local matching algorithm is introduced to significantly improve the matching performance in SAR images. Experiments on different pairs of SAR images show that the proposed method gives better performance than the existing SAR image registration methods.