# Integrated Fusion Network for Hyperspectral, Multispectral and Panchromatic Data Fusion
## 4 Experimental Results
### Experimental Setting
It measures the emptiness of the image The balance between inter resolution and spectral resolution. Ergas stands for root mean square relative error measurement, which is defined as root mean square error RMSE and Average Absolute Spectral Error (AASE) Multiply by an amplification factor. The formula is as follows:  
\[ERGAS=(\text{RMSE}/AASE)\times K \tag{5}\]  
where \(RMSE\) represents the root mean square error between spectral channels, \(AASE\) represents the average absolute error of spectral channels, and \(K\) represents the amplification factor. (3) SAM: The SAM is employed to measure the spectral differences between pixels in images. It assesses the disparity between the spectral accuracy of there constructed image and the original image. Lower SAM values indicate a closer spectral resemblance between the reconstructed image and the original image, thus indicating higher reconstruction quality. The calculation formula is as follows:  
\[SAM=\text{cos}^{-1}\left(\frac{\sum_{i=1}^{n}(x_{i}\cdot\hat{x})}{\sqrt{\sum_{ i=1}^{n}\text{x}_{i}^{2}\cdot\sqrt{\sum_{i=1}^{n}\hat{x}^{2}}}}\right) \tag{6}\]  
where \(\text{x}_{i}^{2}\) and \(\text{x}^{2}\) represent the spectral vectors of the corresponding pixels in the two images, respectively, and \(n\) is the dimension of the spectral vector. (4) PSNR: The PSNR is one of the commonly used metrics for assessing image reconstruction quality, It evaluates the degree of image distortion by comparing the mean square error between the pixel values of the original image and the reconstructed image. A higher PSNR value indicates less difference between there constructed image and the original image, thus indicating higher reconstruction quality. The calculation is as follows:  
\[PSNR=10\log_{10}\left(\frac{255^{2}}{MSE}\right) \tag{7}\]  
where \(MSE\) is the mean square error which can be calculated as follows:  
\[MSE=\frac{1}{H\times W}\sum_{i=0}^{H-1}\sum_{j=0}^{W-1}\left[I(i,j)-R(i,j) \right]^{2} \tag{8}\]  
where \(H\times W\) denote the size of the image, \(I\) and \(R\) denote the original and reconstructed image. (5) RASE: The RASE is a tool for quantifying the accuracy of spectral data, which evaluates the accuracy of spectral data by calculating the average relative error between estimated and actual values. \[RASE=\frac{1}{ME}\sqrt{\frac{1}{N}\sum_{i=1}^{N}RMSE^{2}(B_{i})} \tag{9}\]  
where \(ME\) represents the mean of \(N\) spectral bands of the reference image, and \(RMSE(B_{i})\) is the root mean square error of the ith spectral band \(B_{i}\). The 12 th generation Intel(r) Core(r) [EMAIL_ADDRESS] is used in all tests, on a laptop computer with GHz, GeForce GTX 4060 GPU, and 8 GB of memory, using the Python3.10 framework. The experiment utilizes the comparison approach with default parameter settings. HyperPNN is trained using the same dataset as IFNPMH, utilizing deep learning techniques. Furthermore, the parameters in IFNPMH are modified using the AdamOptimizer algorithm, with an initial learning rate of 0.001. Meanwhile, we adjusted the batch size of any given dataset with different hyperspectral bands to train the model. The training batch size for the Chikusei dataset was 40, while the training batch size for the Houston dataset was set to 60. Ultimately, IFNPMH necessitates around 200 iterations to attain exceptional performance.