# Integrated Fusion Network for Hyperspectral, Multispectral and Panchromatic Data Fusion
## 4 Experimental Results
### Experiments with Simulated Data
#### 4.2.2 Comparative Experiments on Houston Data  
This paper systematically chooses a specific piece of the Houston dataset, measuring 3150 \(\times\) 1050 \(\times\) 48, in order to evaluate the efficacy of IFNPMH. We divided the chosen image into 147 smaller images, each measuring 150 \(\times\) 150 \(\times\) 48. In the comparative experiment using Houston data, the training data pairs for HSI, MSI, and PAN were input with dimensions of 25 \(\times\) 25 \(\times\) 112, 50 \(\times\) 50 \(\times\) 4, and 150 \(\times\) 150 \(\times\) 1, respectively. There were a total of 147 Houston data comparison tests in total. The first 120 pairs were used for training, the next 20 pairs for validation, and the remaining 7 pairs for testing. The train loss and valid loss during the training process are shown in Figure 8.  
Figure 9 displays a visual comparison of the various approaches using the Houston data. The reference image is denoted by HHSI. We chose the 24 th, 10 th, and 5 th bands in order to produce color images. According to Figure 9a, PC exhibits greater spectrum distortion compared to other approaches, and the total disparity between PC and the original image is greater. In Figure 9, it can be observed that FUSE, MAPSMM, and SFIMHS lack spatial smoothness and have coarse spatial details. Conversely, HyperPNN, HMPCNMF, and IFNPMH display distinct spatial details, although there is some spectral distortion. Notably, IFNPMH in Figure 9 exhibits clear spatial details with minimal spectral distortion. When comparing the 8 images in Figure 9, it can be shown that IFNPMH is the most similar to the reference image and produces the best fusion result, thus demonstrating the efficiency of IFNPMH. For instance, in the magnified section of Figure 9, the larger portion of IFNPMH demonstrates a notable superiority compared to other methodologies. This conclusion can be confirmed by examining the residual images and calculating the mean absolute errors between the fusion results and the reference image, as depicted in  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **RMSE** & **ERGAS** & **SAM** & **PSNR** & **RASE** \\ \hline PC & 1.85 & 25.58 & 13.31 & 35.09 & 17.59 \\ GLP & 1.66 & 22.33 & 13.12 & 36.28 & 15.35 \\ Hysure & 1.39 & 19.39 & 9.51 & 37.50 & 13.32 \\ FUSE & 1.68 & 24.71 & 9.93 & 35.40 & 16.99 \\ MAPSMM & 1.69 & 24.93 & 9.90 & 35.30 & 17.13 \\ SFIMHS & 1.85 & 27.93 & 11.05 & 34.33 & 19.21 \\ HyperPNN & 0.88 & 11.78 & 6.96 & 41.83 & 8.10 \\ HMPCNMF & 0.61 & 8.16 & 4.60 & 45.02 & 5.61 \\ IFNPMH & **0.48** & **6.49** & **3.46** & **47.00** & **4.47** \\ Expected Value & 0 & 0 & 0 & +\(\infty\) & 0 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Average performance of the compared methods over 10 testing images of Chikusei data set. (Bold represents the optimal value of the indicator).  
Figure 10. The residual values of HyperPNN, HMPCNMF, and IFNPMH are comparatively lower than those of other methods, with IFNPMH exhibiting the highest performance. This further validates the effectiveness of IFNPMH.  
Figure 8: The train loss and valid loss during the training process of the Houston dataset.  
Figure 9: Visual comparison of fusion results on the simulated Houston data set: (**a**) PC, (**b**) GLP, (**c**) Hysur, (**d**) FUSE, (**e**) MAPSMM, (**f**) SFIMHS, (**g**) HyperPNN, (**h**) HMPCNMF, (**i**) IFNPMH, (**j**) HHSI. (The red box represents a partially enlarged area in the image, in order to better display the details in the image).  
The average metrics for the compared procedures are provided in Table 2, based on data from 7 testing images. Just as the Chikusei dataset, the SAM index of PC is higher compared to other approaches, suggesting that the fusion results of PC exhibit the most pronounced spectral distortion. HyperPNN, HMPCNMF, and IFNPMH outperform PC, GLP, physique, FUSE, MAPSMM, and SFIMHS. IFNPMH outperformed all other 8 advanced approaches in terms of quantitative measures, demonstrating its better fusion performance and effectiveness.