# Integrated Fusion Network for Hyperspectral, Multispectral and Panchromatic Data Fusion
## 4 Experimental Results
### Experiments with Simulated Data
#### 4.2.1 Comparative Experiments on Chikusei Data  
Our research employs a random selection process to choose 1600 \(\times\) 1600 \(\times\) 112 components from the Chikusei dataset in order to assess the efficacy of IFNPMH. We divided the chosen images into 100 smaller images, each measuring 160 \(\times\) 160 \(\times\) 112. In the comparative experiment of Chikusei dataset, the training data pairs for HSI, MSI, and PAN were input with dimensions of 40 \(\times\) 40 \(\times\) 112, 80 \(\times\) 80 \(\times\) 4, and 160 \(\times\) 160 \(\times\) 1, respectively. The Chikusei data comparison experiments consisted of 100 pairings in total. The first 80 pairs are designated as the training dataset, the next 10 pairs are utilized as the validation dataset, and the last 10 pairs are assigned to the testing dataset. The train loss and valid loss during the training process are shown in Figure 5.  
Figure 6 displays a visual comparison of the various approaches used on the Chikusei data. The reference image is represented by HHSI. To generate color photos, we chose bands 46, 26, and 6. According to Figure 6, PC and GLP exhibit higher levels of spectrum distortion compared to other approaches, resulting in the poorest fusion outcomes. The spectral resolution of FUSE, MAPSMM, and SFIMHS surpasses that of PC and GLP, but, their spatial resolution is subpar. Hysure and HyperPNN provide precise spatial information, however there is some spectral distortion. The performance of HMPCCNMF and IFNPMH surpasses the fusion approach that utilizes only two specified input sources. Out of all the options, IFNPMH provides the most effective fusion effect when comparing to Figure 6. The Figure 6a-j are enlarged due to the effective utilization of the features of HSI, MSI, and PAN images by HMPCCNMF and IFNPMH. As a result, the enlarged images produced by HMPCCNMF and IFNPMH exhibit improved spatial details and more precise spectrum information compared to previous approaches. Upon comparing the magnified images of HMPCCNMF and IFNPMH, it was determined that the fusion outcomes of IFNPMH were superior to those of HMPCCNMF, hence demonstrating the efficacy of IFNPMH. This finding can be corroborated by examining the residual images and calculating the mean absolute errors between the fusion results and the reference image, as depicted in Figure 7.  
Figure 5: The train loss and valid loss during the training process of the Chikusei dataset. According to this figure, the residual picture, obtained by subtracting the fused image from the reference image, indicates a high overall residual PC value. The substantial disparity in spectral information between PAN and HSI leads to pronounced spectral distortion. The residual values of GLP, Hycure, FUSE, MAPSMM, and SFIMHS primarily cluster along the boundaries of the features. HyperPNN, HMPCNMF, and IFNPMH exhibit generally low residual values, with IFNPMH being the most effective, thereby further validating its efficacy. The average quantitative metrics for the compared methods are presented in Table 1, based on 10 testing images. The final row of Table 1 displays the optimal values for each  
Figure 6: Visual comparison of fusion results on the simulated Chikusei data set: (**a**) PC, (**b**) GLP, (**c**) Hysure, (**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). Figure 7: Residual images of fusion results on the simulated Chikusei data set: (**a**) PC, (**b**) GLP, (**c**) Hysure, (**d**) FUSE, (**e**) MAPSMM, (**f**) SFIMHS, (**g**) HyperPNN, (**h**) HMPCNMF, (**i**) IFNPMH. indicator, with the most favorable outcomes from the comparison procedures emphasized in bold red. According to the data in this table, the HyperPNN deep learning approach outperforms the other six traditional fusion methods in all quantitative indicators in the fusion results of paired data sources.