Novel Adaptive Component-Substitution-Based Pan-Sharpening Using Particle Swarm Optimization

Component substitution (CS) technique is a famous framework for merging multispectral (MS) and panchromatic (Pan) images. The synthetic intensity component is important in the CS fusion framework. In this letter, we propose an optimization model to obtain the adaptive weights. The adaptive weights are computed by maximizing an objective function, which measures the radiometric similarity between the low-scale intensity image and the spatially degraded Pan image. Correlation coefficient, mean-structural-similarity index, and mutual information are used as the similarity criteria, respectively. A particle-swarm-optimization algorithm is adopted to solve the single objection optimization problem. The proposed CS framework is compared with popular CS-based fusion methods. Visual analysis and quality results demonstrate that the proposed adaptive CS fusion framework has superior performance.