Skip to main content
Log in

A Metaheuristic Optimization-Based Solution to MTF-GLP-Based Pansharpening

  • Original Article
  • Published:
PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science Aims and scope Submit manuscript

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12

Similar content being viewed by others

References

Download references

Acknowledgements

All pansharpening methods except the DRPNN were employed with the MATLAB scripts provided through https://openremotesensing.net/. The DRPNN method was applied using the MATLAB script provided by Wei et al. (2017) through https://github.com/Decri/DRPNN-Deep-Residual-Pan-sharpening-Neural-Network. We would like to thank Liu et al. (2020) for providing the source images of the site 2 through https://github.com/zhysora/PSGan-Family. We also thank the Department of Geomatics Engineering of Karadeniz Technical University for providing the imagery data for the other sites.

Funding

Not applicable.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Cigdem Serifoglu Yilmaz.

Ethics declarations

Conflict of interest

The authors declare no competing interests.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Serifoglu Yilmaz, C., Gungor, O. A Metaheuristic Optimization-Based Solution to MTF-GLP-Based Pansharpening. PFG 91, 245–272 (2023). https://doi.org/10.1007/s41064-023-00248-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s41064-023-00248-w

Keywords

Navigation