Enhanced Pansharpening Using Curvelet Transform Optimized by Multi Population Based Differential Evolution

Mustafa Hüsrevoğlu; Ahmet Emin Karkınlı

doi:10.32628/CSEIT24104116

Authors

Mustafa Hüsrevoğlu Geomatics Engineering, Nigde Omer Halisdemir University / Engineering Faculty, Nigde, Türkiye Author
Ahmet Emin Karkınlı Geomatics Engineering, Nigde Omer Halisdemir University / Engineering Faculty, Nigde, Türkiye Author

DOI:

https://doi.org/10.32628/CSEIT24104116

Keywords:

Pansharpening, Curvelet Transform, Multi Population Based Differential Evolution, Landsat ETM, Image Processing

Abstract

In this study, a pansharpening process was conducted to merge the color information of low-resolution RGB images with the details of high-resolution panchromatic images to obtain higher quality images. During this process, weight optimization was performed using the Curvelet Transform method and the Multi Population Based Differential Evolution (MDE) algorithm. The proposed method was tested on Landsat ETM satellite image. For Landsat ETM data, the RGB images have a resolution of 30m, while the panchromatic images have a resolution of 15m. To evaluate the performance of the study, the proposed MDE-optimized Curvelet Transform-based pansharpening method was compared with classical IHS, Brovey, PCA, Gram-Schmidt and Simple Mean methods. The comparison process employed metrics such as RMSE, SAM, COC, RASE, QAVE, SID, and ERGAS. The results indicate that the proposed method outperforms classical methods in terms of both visual quality and numerical accuracy.

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