# Swin-MRDB: Pan-Sharpening Model Based on the Swin Transformer and Multi-Scale CNN
## 2 Methods  
### Existing Methods  
There are various existing pan-sharpening models with fusion strategies based on different algorithms. In this section, several of the most widely used models are described in detail and selected as comparison models to evaluate the superiority of the proposed Swin-MRDB method. Table 1 briefly describes the comparison algorithms. Among the traditional fusion methods, principal component analysis (PCA) [24] is widely used. PCA is an orthogonal linear change based on the amount of information, and the algorithm uses a linear transformation method to project the data into new spatial coordinates, transforming the massive spectral data into a small number of several components to retain the maximum information of the original data while reducing dimensionality. The pan-sharpening process of PCA is to first find the feature components of the covariance matrix between the bands of the hyperspectral image, followed by matching the histogram of the panchromatic image to the first principal component, and then replacing the first principal component of the covariance matrix with the panchromatic image. Finally, the fused result is transformed back using PCA inverse transform to obtain the final fused image. The Gram-Schmidt (GS) [25] fusion method can avoid the inconsistency of the spectral response range caused by the traditional fusion methods and maintain the consistency of the spectral information of the image before and after the fusion using the statistical analysis method for the optimal matching of each band involved in the fusion. The GS algorithm performs pan-sharpening fusion in four steps. First, a low-resolution band is replicated to create a panchromatic band. Second, a Cram-Schmidt transformation is used on both the panchromatic and multiband. Third, the high spatial resolution panchromatic band replaces the transformed first band. Lastly, the fusion map is obtained through the application of the Cram-Schmidt inverse transform. The IHS [26] algorithm is one of the widely used algorithms in the field of image fusion, which first resamples hyperspectral images and converts them from RGB to HIS (brightness, hue, saturation) space, then replaces the I component with a panchromatic image, and finally obtains the fused image via inverse conversion. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Algorithm** & **Technology** & **Algorithm Type** & **Reference** \\ \hline PCA & Band replacement & traditional method & [20] \\ GS & Band replacement & traditional method & [21] \\ IHS & Spatial transformation & traditional method & [26] \\ PNN & Two-stage network & deep learning & [23] \\ TFNET & Two-stream network & deep learning & [24] \\ MSDCNN & Two-stream network & deep learning & [25] \\ SRPPNN & Characteristic injection network & deep learning & [26] \\ \hline \hline \end{tabular}
\end{table} 
Table 1: An abbreviated description of the comparison algorithm. With the continuous development of deep learning, the technique has become the mainstream pan-sharpening method, and more and more scholars have proposed different feature extraction and fusion strategies to fuse panchromatic and spectral images. The convolutional neural network (CNN) is a popular deep learning model. It excels at processing multi-dimensional data by keeping the input and output in the same dimension. CNNs comprise three layers: convolutional, activation function, and pooling. Each layer has a specific function for processing data, and multiple layers can create a nonlinear transformation to map input and output data. This makes CNNs an essential tool in the field of pan-sharpening. In this paper, a few of the most typical models are selected for a detailed explanation, and these are chosen as the comparison models in this paper. PNN [27] is the first model to apply convolutional neural networks to the field of pan-sharpening, which is achieved by a simple and efficient convolutional architecture with three layers. TFNet [28] is a typical two-stream convolutional pan-sharpening network.