## 3 Proposed Method  
### The Overall Architecture of DCMFN  
First of all, we begin by detailing the overall architecture of the DCMFN. Following this, we discuss the individual components of the feature extraction modules designed for PAN and MS images, known as PFEM and MFEM. These modules incorporate SAB \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Characteristics** & **Advantage** & **Limitation** \\ \hline \multirow{3}{*}{PNN} & Bringing CNN to the & Good detail & \multirow{2}{*}{Limited adaptability to \multirow{2}{*}{complex scenarios.}} \\  & pansharpening for the & retention\(\_\)Strong & & \\  & first time. & & adaptability. & \\ \hline \multirow{6}{*}{PanNet} & The up-sampled MS & \multirow{2}{*}{Enhances the detail of \multirow{2}{*}{for computing resources}} \\  & images propagate & & \\  & spectral information, & & \\  & and the network is & & \\  & trained in a high-pass & & \\  & domain to retain spatial & & \\  & structure. & & \\ \hline \multirow{6}{*}{MSDCNN} & TFNet & Adopt dual-stream & Reduced spectral & Training and \\  & network architecture. & distortion and loss of & \\  & spatial detail. & & \\ \hline \multirow{6}{*}{MSUCNN} & The convolution & \multirow{2}{*}{Texture features are \multirow{2}{*}{richer than single-scale convolution.}} & \multirow{2}{*}{Remote sensing images} \\  & matrices of different & & \\  & scales capture & & \\  & multi-scale feature & & \\  & information. & & \\ \hline \multirow{6}{*}{MSUCNN} & The convolution kernel & \multirow{2}{*}{The processing of \multirow{2}{*}{extreme bright spots on}} \\  & converts the spectral & & \\  & information of the & Better performance than & \\  & panchromatic image into & direct upsampling. & Sometimes pixel noise \\  & high-frequency details & & \\  & in the spatial domain. & & \\ \hline \multirow{6}{*}{GGPCRN} & The interactive & \multirow{2}{*}{Adapt to detail \multirow{2}{*}{enhancement needs in \multirow{2}{*}}} & \multirow{2}{*}{Requires high  computing resources.} \\  & connection realizes the & & \\  & collaborative & & \\  & optimization of & & \\  & spectral–spatial features. & & \\ \hline \multirow{6}{*}{GGPCRN} & The cross-modality & \multirow{2}{*}{Performs better than the \multirow{2}{*}{traditional CNN method}} & \multirow{2}{*}{The detail is not rich \multirow{2}{*}} \\  & information of PAN and & & \\  & MS was fused using the & & \\  & cross-attention module. & & \\ \hline \multirow{6}{*}{SSIN} & The dual flow network & \multirow{2}{*}{The detail for \multirow{2}{*}{extracts spatial and \multirow{2}{*}{presently, and uses \multirow{2}{*}{separately, and uses \multirow{2}{*}{separately, and uses \multirow{2}{*}{separately, and uses \multirow{2}{*}{separately, and uses \multirow{2}{*}{separately, and uses \multirow{2}{*}{separately, and \multirow{2}{*}{separately,}}}}}}}} \\  & & extracts spatial and \multirow{2}{*}{separately, and uses \multirow{2}{*}{separately, and uses \multirow{2}{*}{separately, and uses \multirow{2}{*}{separately, and uses \multirow{2}{*}{separately, and \multirow{2}{*}{separately,}}}}} \\  & spectral information & & \\  & respectively, and uses & \\  & SSA and IIB modules to & & \\  & realize the interaction & & \\  & and fusion of the two & & \\  & kinds of information. & & \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Deep learning-based method for pansharpening. for effective feature extraction. Next, we provide an overview of the CMFFM, which includes the Successive Swin Transformer Module (SSTM). Finally, we present the reconstruction module. The architecture of the DCMFN is depicted in Figure 1, which illustrates its overall structure. DCMFN consists of the following three parts. The first part is the PAN (or MS) Feature Extraction Module. In this module, there are two independent feature extraction modules. We can use these two modules to extract modality-specific features of PAN and MS, respectively. The second part is CMFFM. The modality-specific features extracted from PFEM and MFEM are input into this module.