Abstract
Improving the matching reliability of multi-sensor imagery is one of the most challenging issues in recent years, particularly for synthetic aperture radar (SAR) and optical images. It is difficult to deal with the noise influence, geometric distortions, and nonlinear radiometric difference between SAR and optical images. In this paper, a method for SAR and optical images matching is proposed. First, interest points that are robust to speckle noise in SAR images are detected by improving the original phase-congruency-based detector. Second, feature descriptors are constructed for all interest points by combining a new Gaussian-Gamma-shaped bi-windows-based gradient operator and the histogram of oriented gradient pattern. Third, descriptor similarity and geometrical relationship are combined to constrain the matching processing. Finally, an approach based on global and local constraints is proposed to eliminate outliers. In the experiments, SAR images including COSMO-Skymed, RADARSAT-2, TerraSAR-X and HJ-1C images, and optical images including ZY-3 and Google Earth images are used to evaluate the performance of the proposed method. The experimental results demonstrate that the proposed method provides significant improvements in the number of correct matches and matching precision compared with the state-of-the-art SIFT-like methods. Near 1 pixel registration accuracy is obtained based on the matching results of the proposed method.
Highlights
Synthetic aperture radar (SAR) imaging systems, which can operate normally in clouds, are increasingly being used for earth observation research because of developments in spatial information technologies
Image Matching Based on Descriptor Similarity and Geometric Constraint
All the experimental results and analysis in this study show that the proposed method can address the problems in both SAR/SAR image matching and SAR/optical image matching better than the
Summary
Synthetic aperture radar (SAR) imaging systems, which can operate normally in clouds, are increasingly being used for earth observation research because of developments in spatial information technologies. SAR and optical imaging systems have essential differences, and different objects’. Information can be obtained from these two types of systems. SAR datasets alone or optical datasets alone usually do not perform well in some applications [1,2]. The fusion of SAR and optical images can provide complementary information that is beneficial for the applications [3,4,5]. Image matching is a fundamental issue in fusion and data integration. Image matching methods can be classified into two categories: area-based methods and feature-based methods [6].
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