Research Information System
PFG-JOURNAL OF PHOTOGRAMMETRY REMOTE SENSING AND GEOINFORMATION SCIENCE, vol.91, no.4, pp.245-272, 2023 (SCI-Expanded)
In recent years, the pansharpening strategies employing the Generalized Laplacian Pyramid (GLP) based on Gaussian filters that match the Modulation Transfer Function (MTF) of the source multispectral (MS) sensor have attracted attention in remote sensing community. The MTF-GLP-based pansharpening methods differ from each other in the way they obtain the injection coefficients, which are used to transfer the spatial details of the source panchromatic (PAN) image into the source MS image. Investigation of the pansharpening literature showed that the MTF-GLP-based pansharpening strategies generally estimate the injection coefficients using statistics-based deterministic approaches, which leads to a difficulty in identifying the non-linear relationship between the source MS and PAN data. Hence, this study proposes a metaheuristic optimization-based solution to this problem. The proposed method estimates the optimum injection coefficients through the Multi-Objective Symbiotic Organism Search (MOSOS) algorithm, which has been proven to efficiently find the optimum solutions in very complex search spaces. The success of the presented method was qualitatively and quantitatively tested on four test sites against several widely used pansharpening techniques. The experiments revealed that the presented approach did not only outperform some of the commonly used MTF-GLP-based methods, but also some of the other Multiresolution Analysis (MRA)-based, component substitution (CS)-based, deep learning (DL)-based, and variational optimization (VO)-based pansharpening methods.