Optical and SAR image registration via improving implicit similarity

Optical and synthetic aperture radar (SAR) image registration has become a research focus in the area of multisensory image processing for their information complementarity and feature difference. Based on the structural similarity between images, registration via implicit similarity simplifies the traditional feature matching process as a migration of the feature points and the iterative search of registration parameters on a single image. This method provides a new idea for optical and SAR image registration. As a result, the Canny operator is adopted to modify extraction process of feature points. The joint Markov model (JMM) is employed to improve denoising quality of SAR image. The search process of registration parameters is optimized with the modified quantum particle swarm optimization (QPSO) algorithm, and the optical and SAR image registration is finally realized. The experiment proves that the improved implicit similarity algorithm on optical and SAR image registration can reach a high accuracy of pixel level or even sub-pixel level.