Author:
Mustafa Hüsrevoğlu ,Ahmet Emin Karkınlı
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.
Reference42 articles.
1. L. Loncan et al., "Hyperspectral pansharpening: A review," IEEE Geoscience and remote sensing magazine, vol. 3, no. 3, pp. 27-46, 2015.
2. B. Aiazzi, L. Alparone, S. Baronti, A. Garzelli, and M. Selva, "Twenty-five years of pansharpening: A critical review and new developments," Signal and Image Processing for Remote Sensing, 2nd Edition, no. Cap. 27, pp. 533-548, 2012.
3. M. Ehlers, S. Klonus, P. Johan Åstrand, and P. Rosso, "Multi-sensor image fusion for pansharpening in remote sensing," International Journal of Image and Data Fusion, vol. 1, no. 1, pp. 25-45, 2010.
4. S. Rahmani, M. Strait, D. Merkurjev, M. Moeller, and T. Wittman, "An adaptive IHS pan-sharpening method," IEEE Geoscience and Remote Sensing Letters, vol. 7, no. 4, pp. 746-750, 2010.
5. M. Ghadjati, A. Moussaoui, and A. Boukharouba, "A novel iterative PCA–based pansharpening method," Remote sensing letters, vol. 10, no. 3, pp. 264-273, 2019.