# Integrated Fusion Network for Hyperspectral, Multispectral and Panchromatic Data Fusion
## 3 Proposed Method
### Network Architecture  
In this paper, the PNN architecture is used as the extraction module, which is originally used for image super-resolution [36], as shown in Figure 2. Compared to residual networks, PNN is characterized by its simplicity and ease of training. The extraction module consists of three convolutional layers. The filter sizes for these layers are \(5\times 5\), \(3\times 3\), and \(1\times 1\). The stride is set to 1 with padding, and the numbers of extracted feature maps in each layer are set to 128, 256 and 64, respectively. Furthermore, the PNN architecture is enhanced by incorporating skip connections, which are inspired by dense networks. The skip connections facilitate the transmission of more intricate information to the succeeding layers, enabling the network to fully exploit valuable information and train quickly. Figure 3 illustrates the structure of the Attention mechanism module, which consists of CBAM blocks. These blocks encompass both channel attention mechanism blocks and spatial attention mechanism blocks. The Channel Attention Module simultaneously performs max pooling and average pooling on the input feature maps. By performing max  
Figure 1: Network structure of IFNPMH. Figure 2: Network structure of Extraction module. pooling or average pooling operations on the input feature maps with shapes (B, C, H, W), the spatial information of each channel (C) is compressed, resulting in an output with shapes (B, C, 1,1). In this process, for each channel, its spatial information is compressed into a single value by max pooling or average pooling operations, thereby achieving compression and extraction of global spatial information. This step aims to concentrate the information on the feature map onto the channel, so as to better capture the feature information of the input feature map on the channel. By utilizing these two features, the network's representation ability can be greatly improved. The Spatial Attention Module applies average pooling and max pooling along the channel axis to the input feature map. Through these operations, the channel dimension (C) of the input tensor can be compressed to 1, thereby integrating global channel information into a single channel feature map with the shape of (B, 1, H, W). In this process, for each sample (B), the model will average and pool the features of the sample on the channel, thereby achieving compression and merging of global channel information. This operation helps reduce the number of parameters, decrease computational complexity, while preserving important channel feature information. Concatenate the two obtained matrices on the channels and use a convolutional layer to reduce the number of channels to 1 again, so that all the obtained feature information is distributed on one channel. Then, generate a weight vector using the sigmoid function from the output of the convolutional layer. CBAM is the process of multiplying the channel attention weights obtained from the Channel Attention Module with the original input feature map. This step is used to adjust the feature values of each channel, emphasizing the information of important channels and suppressing the information of unimportant channels. Multiply the spatial attention weights obtained in the Spatial Attention Module by the feature map obtained through the channel attention mechanism to obtain the final output result. The channel attention mechanism block acquires the weights of each channel, allowing the model to dynamically modify the significance of channel features and improve its capability to represent various features. The spatial attention mechanism block calculates the weights for each spatial location, allowing the model to dynamically alter the significance of spatial position information and improve its capacity to simulate different positions. The final adaptive feature response can be generated by multiplying the channel attention mechanism block with the spatial attention mechanism block.