# Integrated Fusion Network for Hyperspectral, Multispectral and Panchromatic Data Fusion
## 4 Experimental Results
### Experimental Setting  
The Chikuse and Houston hyperspectral datasets are utilized for training in image fusion in this section. The Chikusei dataset was acquired using Headwall Hyperspec-VNIR-C sensors during aerial surveys conducted in Japan. The article excludes the frequency  
Figure 4: Network structure of Reconstruction module. bands 1-14 and 127-128 from the collection, resulting in a dataset size of 2517 \(\times\) 2335 \(\times\) 112 and a spectral range of 0.36-1.018 \(\upmu\)m. The dataset utilized in the 2018 IEEE GRSS Data Fusion competition is referred to as DFC2018 Houston. The dataset was generated and publicly released by Dr. [PERSON]'s laboratory at the University of Houston. The dataset has dimensions of 4172 \(\times\) 1202 \(\times\) 48 and covers a spectral range from 0.38 to 1.050 \(\upmu\)m.  
The reference image utilized in the simulation experiment was the original HSI obtained from the hyperspectral dataset mentioned above. This article employs Gaussian blur to blur the reference image and subsequently downsamples the blurred image for the purpose of generating test HSI. The process of generating a test PAN involves downsampling the reference image via spectral resampling, followed by averaging the spectral channels to create the test PAN. The test MSI is generated by applying spectral resampling and Gaussian blur to blur the spectral resampled picture, followed by spatial downsampling of the blurred image. This article applies a 7 \(\times\) 7 Gaussian low-pass filter to blur the images in all simulated datasets. Each dataset has a unique spatial downsampling rate. In the Chikuse dataset, the spatial resolution ratio between PAN and HSI is 4, while the geographical resolution ratio between PAN and MSI is 2. The Houston dataset has a spatial resolution ratio of 6 between PAN and HSI, and a spatial resolution ratio of 3 between PAN and MSI. Each dataset also employs a distinct spectral resampling approach. This article utilizes the spectral response function of IKONOS for the Chikusei dataset and the spectral response function of Deimos-2 for the Houston dataset. We compare IFNMPH with eight state-of-the-art methods, including the classical FHP methods (Spectral Sharpening (PC) [37], Generalized Laplacian Pyramid (GLP) [38] and Through subspace based regularization (Hysure) [39]), FHM methods (Fast fusion based on Sylvester equation (FUSE) [40], Resolution Enhanced Random Mixture Model for Hyperspectral Images (MAPSMM) [41] and Smooth Filter Based Supersharpening Intensity Modulation (SFIMHS) [32]), and a deep learning based fusion of HSI and PAN (via Spectral prediction convolutional neural network (HyperPNN) [28]). This paper aims to resample the fusion results obtained from the FHM method to match the spatial resolution of the fusion results obtained from the FHP method, in order to facilitate analysis. This research examines the scarcity of deep learning approaches in FHMP and compares several contemporary FHMP methods, such as the Fusion of HSI, MSI, and PAN based on CNMF (HMPCNMF) [34]. We have considered five evaluation indices to quantitatively assess the fusion performance of comparative methods in simulation experiments. These indicators include root mean square error (RMSE), relative dimensionless global error (ERGAS) [42], spectral angle mapper (SAM) [43], peak signal-to-noise ratio (PSNR), and relative average spectral error (RASE) [44]. (1) RMSE: The RMSE is the root mean square error, which is one of the indicators for measuring image quality. The calculation principle is to square the difference between the true value and the predicted value, then add it up and average it, and finally root it. The formula is as follows:  
\[RMSE=\sqrt{\frac{1}{n}\sum_{i=1}^{n}(z_{i}-\hat{z_{i}})^{2}} \tag{4}\]  
where \(z_{i}\) is the true value, \(\hat{z_{i}}\) is a predicted value, \(i\) is the sample size. The smaller the \(RMSE\) value, the more similar the image. (2) ERGAS: The Ergas formula is an indicator used to test the resolution quality of remote sensing images.