Panchromatic and Multispectral Image Fusion for Remote Sensing and Earth Observation via Swarm Intelligence Optimization Algorithm - Qi, Shuai-Cheng; Huang, Ji-Lai; Wang, Jie-Sheng; Xu, Zi-Rui (Engineering letters, 2025)
In the field of remote sensing image processing, (he fusion technique of Panchromatic (PAN) image and Multispectral (MS) images has attracted much attention. Compared with the source image, the fusion image has higher spatial and spectral resolution, and it has been widely used in various applications of image interpretation and preprocessing. In this paper, swarm intelligent optimization algorithm is used to sharpen MS and Pan. The algorithms used include Particle swarm optimization (PSO), Simulated Annealing (SA), Cooperation search algorithm (CSA), Polar lights optimizer (PLO), Kookaburra Optimization Algorithm (KOA), and Arctic puffin optimization (APO). By preprocessing the original image to normalize it, the edge detection function of the panchromatic image and the multispectral image is extracted linear combination is performed, and the trade-off parameter vector of linear combination is calculated by various optimization algorithms, to obtain the fused image. The experimental results on Pleiades, WorldView-2, and QuickBird satellites show that the multi-population optimization algorithm has outstanding comprehensive performance compared with the traditional methods in the subjective visual effect and objective performance evaluation of multi-spectral image fusion.