**5. Conclusions**

This research proposed an effective, component substitution-based image Pan sharpening method that uses CIELab color space for Pan sharpening of the VHR Pléiades satellite images. Ten test sites with different landscape characteristics were selected to evaluate the performance of the proposed method in conjunction with six common Pan sharpening algorithms; namely, GS, HCS, IHS, EHLERS, NNDiffuse and GIHS. The comparative evaluation results from Pléiades VHR images supports that the proposed CS algorithm is powerful and ensures better performance compared to the other Pan sharpening methods according to the spectral and spatial accuracy assessment procedures and the visual interpretation. In addition, results indicated that proposed method provided comparatively consistent results, while the performance of other methods varyied with respect to land surface characteristics of the region. As an example for RMSE metric, the best values among the all ten sites were obtained for forest and vegetated areas. Pan sharpening in urban areas resulted in coarser metric values, which illustrate the impact of different land characteristics on the performance of Pan sharpening algorithms. Characteristics of unique CIELab color space, led to producing similar brightness characteristics in Pan sharpened images compared to original MS image. The nature of L\* component of MS image helps to preserve spectral and spatial information of original MS and Pan images, respectively. Further improvement of the CIELab-based method could be the implementation of this approach for Pan sharpening of satellite images with more than three bands. In addition, further studies are planned to evaluate the performance of CIELab in fusions of satellite images from different sources. Lastly, other accuracy assessment approaches, such as comparisons of classification and segmentation results of Pan sharpened images, could also help future investigations.

**Author Contributions:** conceptualization, A.R., U.A. and C.G.; methodology, A.R., U.A. and C.G.; formal analysis, A.R.; investigation, A.R.; data curation, A.R.; writing—original draft preparation, A.R., U.A. and C.G.; writing—review and editing, A.R. and U.A.; visualization, A.R.; supervision, U.A. and C.G.; project administration, C.G.

**Funding:** This research received no external funding.

**Acknowledgments:** Great appreciation to ITU CSCRS for providing VHR "Pléiades" images. The authors would also like to acknowledge the many useful contributions of Elif Sertel.

**Conflicts of Interest:** The authors declare no conflict of interest.
