## 2 Materials and Methods
### Methodology
#### 2.2.7 Strategy on Image Fusion and Quality Assessment  
Only the selection of image fusion algorithms, VIs and quality indices is not sufficient to analyze the effect of image fusion on VIs properly, so one strategy was made, as shown in Table 4. In this strategy, we included both same-sensor and multi-sensor image fusion with different SRF, SRR and SNR to analyze the result in detail. Different resolutions PAN and MS images were strategically fused, making the same SRR and different SRR, same SRF and different SRF, good SNR and poor SNR to analyze their effects properly. RVI was calculated using band math operation in different resolutions 1 m, 4 m, 8 m and 50 m from respective original or resampled reference MS images (Table 4).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithms** & **Details** & **Advantages** & **References** \\ \hline \multirow{4}{*}{GS} & A generalization of PCA, in which PC1 may be & Spectral characteristics of & \\  & arbitrarily chosen and the remaining components are & lower-spatial-resolution MS & [6; 19; 20] \\  & calculated to be orthogonal or uncorrelated to & data are preserved. & \\ \hline \multirow{2}{*}{Ehlers} & Enhances high-frequency changes such as edges and & Spectral characteristic & \\  & grey level discontinuities in an image. & preserving. & \\ \hline \multirow{2}{*}{PC} & Replacement of PC1 by high-resolution image, which & Gives high-resolution MS & [6; 22; 23; 24] \\  & contains the most information of the image resembles. & images. & \\ \hline \multirow{4}{*}{MHS} & Making forward PCA transform for both intensity & \multirow{2}{*}{Involves both HIS and PCA} & \\  & component(I) and high-resolution panchromatic images & & methods, Preserves both \\ \cline{1-1}  & getting the first component as I’. Matching the & spatial and spectral details. & \\ \cline{1-1}  & histogram of I’ component with I to make I’ and finally & \\ \cline{1-1}  & transferring I’. H, S to RGB true color space. & & \\ \hline \multirow{2}{*}{HPF} & High-frequency spatial content of the pan image is & Preserves a high percentage & \\ \cline{1-1}  & extracted using a high-pass filter and transferred to the & of the spectral characteristics & [22; 23] \\ \cline{1-1}  & resampled MS image. & of the MS image. & \\ \hline \hline \end{tabular}
\end{table}
Table 2: Different image fusion algorithms for the research.  
detailed strategy for all RVI and FVI, as mentioned in Table 4. To evaluate the result, we need quality indices like root mean square error (RMSE), which is a square root of MSE [25]; it is a standard measure of the difference between the reference image and the fused image. The greater the RMSE, the higher the difference between reference and the fused image will be [24,50]. RMSE calculates the amount of change per pixel due to processing. Similarly, the value of correlation coefficient (CC) should be close to +1, which indicates that the reference and fused images are the same. Variation increases when the value of CC is lower [14]. Assessment of objective quality (CC and RMSE) [48] and a variety of visual inspections [26] is necessary for analyzing the effect of image fusion properly.