## 3 Results and Discussion
### Spectral Synthesis
Such a high UIQI value demonstrates high spectral consistency as it considers factors such as loss of correlation, luminance, and contrast distortion. The IMVM algorithm had the best RMSE, RASE, and ERGAS values of 11.674, 3.741, and 1.062, respectively, the lowest amongst the tested pansharpened methods. The pansharpened image maintains almost the same natural color as the original multispectral images and the same level of spatial detail as the original panchromatic images. Results of the assessment also revealed that the IMVM algorithm had the best synthesis as shown by an ERGAS of 3.921, the lowest in the analysis, indicating that the fused image had minimum distortions and is quite similar to the reference image. The RCS algorithm ranked second in the assessment and showed good results in terms of spectral consistency and synthesis. The ability of the algorithm to retain spectral information is shown by a correlation coefficient of 0.855, bias of 0.007, DIV of 1.270, ERGAS of 2.296, UIQI of 0.856, RASE of 8.239, and RMSE of 26.009. The other pansharpening methods that performed comparatively well in terms of spectral consistency were the wavelet principal components, MIHS, and PANSHARP methods. The PCA and Brovey methods produced consistently poor results in terms of spectral consistency as shown by the CC, bias, DIV, ERGAS, UIQI, RASE, and RMSE results. Spectral synthesis is one of the properties that needs to be analyzed under [PERSON]'s three property criteria. As pointed out earlier, our results indicate that the IMVM algorithm produces the best spectral synthesis as shown by a very low ERGAS value of 3.921. Once again, the RCS algorithm ranked second with an ERGAS value of 6.180. Good spectral synthesis results were also obtained by the BAY, EHLERS, GRS, PANSHARP, and MIHS fusion techniques. The spectral synthesis results also revealed the poor performance of the Brovey, CNS, and PCA methods as shown by ERGAS values of 27.034, 25.122, and 19.177, respectively. The third property evaluated in this study in terms of [PERSON]'s three property criteria related to spatial consistency. The correlation coefficient results ranked BAY, GRS, CNS, PANSHARP, RCS, PCA, and MIHS algorithms among the top-performing fusion techniques in terms of spatial consistency. While the Bayer algorithm was considered the best in terms of spectral consistency, most of the algorithms showed high spatial correlation with a correlation coefficient above 0.8 and the wavelet principal component method having the lowest value of 0.542. In contrast to the spectral consistency and synthesis results, the IMVM algorithm did not feature among the top-performing algorithms although it still had a high correlation coefficient of 0.784. This result seems to suggest there is a trade-off between spectral consistency and synthesis with spatial consistency. While the IMVM and RCS pansharpening methods showed superior performance compared to the other fusion methods such as the PANSHARP, MIHS, GRS, wavelet transform, Bayesian, and EHLERS pansharpening techniques, the results of this study clearly show the credibility of these methods in terms of preservation of spectral and spatial information. When selecting the most ideal pansharpening method to use for practical applications, a trade-off is required in terms of factors such as the need for retention of scene radiometry, image sharpness, spatial and spectral consistency, and computational overhead. Color distortion due to pansharpening could be attributed to the broadening of the panchromatic band into the near-infrared wavelength region in some modern sensors [26]. In the case of SPOT 6/7, the panchromatic bands have a spectral range of 450 nm to 745 nm, clearly overshooting the bands in the visible spectrum and encroaching into the near-infrared region that starts from the nominal red edge at 700 nm. This spectral coverage essentially spans over the visible spectrum that contains the blue (450-450 nm), green (530-590 nm), and red (625-695 nm) spectral channels.