# Panchromatic and Multispectral Image Fusion Combining GHS, NSST, and PCA
## 4 Results
### Experimental Results  
#### 4.2.1 Experimental Results with References  
The test in this subsection was conducted using the model with references to compare the effects of different methods. Figure 4 shows the fusion images of GE-1 urban features. Figure 4 shows a down-sampled PAN image; Figure 4 shows an up-sampled image after the down-sampling of the MS image, which is noted as being an EXP (expanded) image; Figure 4 shows the original MS image, which was used as the reference image and is a GT (ground true) image; Figure 4-i show the results under different fusion algorithms; and the subsequent experiments in this section were set up in this manner. It can be seen from the figure that different methods can successfully fuse PAN and MS images. However, the results of the GIHS, Brovey, and GS methods had some spectral distortion, while the spectra of the SIFM, GS, and this paper's method were better maintained. Furthermore, in terms of the details, the accuracy was better retained when using the method proposed in this paper. From the quantitative evaluation results in Table 2, it can also be seen that the AG and SSIM of this method were the highest among the various methods, indicating that this method had the best spatial detail retention, in addition to the best performance for the UIQI and ERGAS indexes, indicating its excellent spectral retention ability. The best performances for CC and SAM were found in the results of the GS and SFIM methods, respectively. However, on the whole, the best results were obtained by the method of this paper. \begin{table}
\begin{tabular}{c c c c} \hline
**Satellite** & **Spatial Resolution** & **Image Fast Size/Pixels** & **Feature Type** \\ \hline GE-1 & 0.46 m PAN, 1.84 m MS & 2048 PAN, 512 MS & Urban \\ WV-4 & 0.31 m PAN, 1.24 m MS & 2048 PAN, 512 MS & Plants and water \\ GF-7 & 0.8 m PAN, 3.2 m MS & 2048 PAN, 512 MS & Farmland \\ GFDM & 0.5 m PAN, 2 m MS & 2048 PAN, 512 MS & Desert \\ \hline \end{tabular}
\end{table} 
Table 1: Experimental datasets. \begin{table}
\begin{tabular}{c c c c c c c} \hline  & **AG** & **SSIM** & **CC** & **UIQI** & **SAM** & **ERGAS** \\ \hline GT & 59.1614 & 1.0000 & 1.0000 & 1.0000 & 0.0000 & 0.0000 \\ EXP & 17.2741 & 0.5551 & 0.8404 & 0.3548 & 6.7234 & 9.4465 \\ GIHS & 33.1363 & 0.8624 & 0.9592 & 0.7738 & 6.5338 & 5.9901 \\ SFIM & 32.7685 & 0.8470 & 0.9510 & 0.7577 & **6.1840** & 5.7623 \\ Brovey & 33.0581 & 0.8555 & 0.9593 & 0.7616 & 6.7234 & 6.1109 \\ GS & 33.0537 & 0.8630 & **0.9596** & 0.7744 & 6.4007 & 5.9720 \\ PCA & 32.9883 & 0.8578 & 0.9580 & 0.7700 & 6.4326 & 6.0185 \\ Proposed & **33.1592** & **0.8757** & 0.9559 & **0.7753** & 6.5093 & **5.6390** \\ \hline \end{tabular}
\end{table} 
Table 2: Objective assessment indexes of the GE-1 image fusion results. The fusion results for the WV-4 images with different algorithms are shown in Figure 5, and the scene mainly contains plants and water. It can be seen from the figure that the methods did not show any obvious color bias, but the sharpness of the SFIM method was significantly lower than the other methods. The clarity of the method presented in this paper was better than that of the other methods. The quantitative results in Table 3 show that this method had the best performance for the AG value, while the SFIM method had the lowest AG value, which is also consistent with the visible results. Moreover, the method in this paper obtained the best performance for the CC and UIQI indexes. In contrast, the SFIM method obtained the best results for SAM and ERGAS, which indicates that the SFIM method had the best spectral retention ability on these data. However, on the whole, the method presented in this paper had the best results. Figure 4: Fusion results for GE-1 images using different methods. Figure 6 shows the fusion results of GF-7 images using different algorithms, and the scene is mainly farmland. For this scene's images, the GIHS, Brovey, GS, and PCA methods showed a more obvious color bias, especially the GIHS and Brovey methods.