MMCMOO: A Novel Multispectral Pansharpening Method

Abstract

From the perspective of optimization, most of the current mainstream remote sensing data fusion methods are based on traditional mathematical optimization or single objective optimization. The former requires manual parameter tuning and easily falls into local optimum. Although the latter can overcome the shortcomings of traditional methods, the single optimization objective makes it unable to combine the advantages of multiple models, which may lead to distortion of the fused image. To address the problems of missing multi-model combination and parameters needing to be set manually in the existing methods, a pansharpening method based on multi-model collaboration and multi-objective optimization is proposed, called MMCMOO. In the proposed new method, the multi-spectral image fusion problem is transformed into a multi-objective optimization problem. Different evolutionary strategies are used to design a variety of population generation mechanisms, and a non-dominated sorting genetic algorithm (NSGA-II) is used to optimize the two proposed target models, so as to obtain the best pansharpening quality. The experimental results show that the proposed method is superior to the traditional methods and single objective methods in terms of visual comparison and quantitative analysis on our datasets.

Keywords

• remote sensing data fusion
• traditional mathematical optimization
• single objective optimization
• multi-objective optimization
• NSGA-II