# Integrated Fusion Network for Hyperspectral, Multispectral and Panchromatic Data Fusion
## 4 Experimental Results
### Ablation Studies  
This paper performs ablation tests on the simulated Chikusei dataset to examine the influence of the \(\lambda\) value in the loss function on the training of IFNPMH networks. The values of \(\lambda\) chosen for this article are 0, 3, 5, 8, and 10. When \(\lambda\) is equal to 0, the loss function solely consists of Lspectral and does not take Lspatial into account. The outcome of the fusion is depicted in Figure 11. In visual comparison, the fusion result is most distorted when the value of \(\lambda\) is 0. The rest of the fusion results are very superior, and the difference with the real image is not obvious. This demonstrates the efficacy of incorporating Lspatial. Figure 12 displays the residual map, which represents the difference between the fused \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **RMSE** & **ERGAS** & **SAM** & **PSNR** & **RASE** \\ \hline PC & 1.51 & 124.13 & 23.62 & 28.83 & 36.19 \\ GLP & 0.55 & 41.51 & 9.16 & 38.34 & 12.10 \\ Hysure & 0.55 & 41.52 & 8.90 & 38.34 & 12.11 \\ FUSE & 0.62 & 46.50 & 9.96 & 37.36 & 13.56 \\ MAPSMM & 0.69 & 51.54 & 10.98 & 36.46 & 15.03 \\ SFIMHS & 0.76 & 56.70 & 11.93 & 35.63 & 16.53 \\ HyperPNN & 0.40 & 30.39 & 6.45 & 41.05 & 8.86 \\ HMPCCNMF & 0.37 & 27.99 & 5.91 & 41.76 & 8.16 \\ IFNPMH & **0.30** & **22.56** & **4.81** & **43.63** & **6.58** \\ Expected Value & 0 & 0 & 0 & +\(\infty\) & 0 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Average performance of the compared methods over 7 testing images of Houston data set. (Bold represents the optimal value of the indicator). Figure 10: Residual images of fusion results on the simulated Houston data set: (**a**) PC, (**b**) GLP, (**c**) Hysure, (**d**) FUSE, (**e**) MAPSMM, (**f**) SFIMHS, (**g**) HyperPNN, (**h**) HMPCNMF, (**i**) IFNPMH. image and the true image, for varying values of \(\lambda\). By analyzing the residual map, we observed that the difference between the fused result and the reference image is greatest when \(\lambda\) is equal to 0. This finding further validates the usefulness of incorporating Lspatial. When the value of \(\lambda\) is set to 5, the fusion result is found to be the most similar to the reference image, thus demonstrating that \(\lambda=5\) is superior than other values. This article conducts a quantitative examination of 10 test images from the Chikusei dataset using five evaluation indicators: RMSE, ERGAS, SAM, PSNR, and RASE. The assessment findings are displayed in Table 3. Based on the quantitative assessment results, the fusion image exhibits the highest disparity from the reference image and the worst fusion effect when \(\lambda\) is set to 0. Conversely, when \(\lambda\) is set to 5, the fusion image displays the smallest disparity from the reference image and the best fusion effect. Observing that \(\lambda\) is equivalent to 5 can enhance the optimal functioning of IFNPMH, motivated us to employ 5 as the default parameter for training our model in the loss function. The qualitative evaluation results indicate that when \(\lambda\) is set to 0, which only considers spectral information loss, the fusion result is inferior compared to when \(\lambda\) is not equal to 0, which considers both spectral information loss and spatial information loss. This suggests that incorporating the spatial information loss component into the loss function is effective and positively impacts network training. The quantitative assessment findings indicate  
Figure 11: Visual comparison of fusion results of IFNPMH under the influence of different \(\lambda\) values: (a) \(\lambda=0\), (b) \(\lambda=3\), (c) \(\lambda=5\), (d) \(\lambda=8\), (e) \(\lambda=10\), (f) HHIS. Figure 12: Residual images of fusion results of IFNPMH under the influence of different parameter \(\alpha\): (a) \(\lambda=0\), (b) \(\lambda=3\), (c) \(\lambda=5\), (d) \(\lambda=8\), (e) \(\lambda=10\).