An Accurate Registration Method Based on Global Mixed Structure Similarity (GMSIM) for Remote Sensing Images

Although remote sensing image registration has been studied for several years, achieving accurate image registration remains a challenging task due to the complicated conditions surrounding remote sensing images. To improve the accuracy and robustness of image registration, we proposed a registration method based on the global mixed structure similarity (GMSIM) measure. This measure mixes the structure similarity in both the frequency domain and the intensity domain because phase-based structure similarity in the frequency domain is sensitive to intensity contrast and spatial translation, and gray-based structure similarity in the intensity domain is efficient to structure change. Feature-based registration methods are used to generate the initial registration parameters. After that, we calculate the final registration parameters by maximizing GMSIM. Quantum-behaved particle swarm optimization (QPSO) is utilized to solve the optimal results of GMSIM due to its high efficiency. The proposed method has been evaluated on several remote sensing images differing in scale, gray, and scene and compared with three state-of-the-art registration methods. Experimental results demonstrate the high accuracy of the proposed scheme.