## 2 Related Work  
Because the pansharpened image has not only a lot of spatial details but also rich spectral information, it is widely used in various object detectors. Current pansharpening methods can be divided into two categories: traditional methods and deep learning-based methods. The characteristics, advantages, and limitations of these methods are summarized in Tables 1 and 2 in this paper. Traditional pansharpening methods include Component Substitution (CS) [11], Multi-Resolution Analysis (MRA) [12], and Variational Optimization (VO) [13]. These algorithms have their characteristics: the component replacement method implements spatial information replacement by transform domain processing, the multi-resolution analysis enhances the image by decomposing and injecting spatial details, and the variational optimization utilizes optimization techniques to solve for the best HRMS image. In addition, there are other fusion methods such as the IHS transform and simple average transform. Traditional pansharpening methods utilize a single pair of LRMS and PAN images to generate a HRMS image at full resolution. However, they often struggle to produce high-quality fused output because they rely on the linear relationship between fused products, which is often flawed [14]. In contrast, deep learning algorithms demonstrate significant potential in image fusion, leveraging their robust nonlinear mapping capabilities, automatic feature learning, and enhanced flexibility and scalability. These advantages are particularly pronounced when working with high-resolution and large-scale datasets. To mitigate the limitations associated with traditional pansharpening algorithms, a variety of deep learning-based methods have been introduced. For example, CNN is utilized in the field of pansharpening, referred to as PNN [15], which was the first to introduce deep learning into this area. PanNet [16] enhances the black-box aspect of deep learning in PNN by ensuring the preservation of spatial information from both PAN and MS images, while simultaneously retaining the spectral characteristics of the MS data. TFNet [17] performs two-stream input on PAN and MS images to extract features and fuse them into compact feature maps to reconstruct pansharpened images. GGPCRN [18] uses gradient characteristics containing rich structural information to guide the pansharpening process, etc. The approach mentioned earlier relies on Convolutional Neural Networks (CNNs). However, these CNN-based methods do not take into account the correlation between the PAN and MS bands. Integrating this correlation into the fusion process is essential to mitigate spectral distortion in high-resolution multispectral (HRMS) images. The current research often overlooks the exploration of cross-modality relationships and neglects the enhancement of modality-aware features in pansharpening [19]. With the emergence of the Swin Transformer architecture [20], [PERSON] et al. introduced self-attention into a two-branch network to extract and merge spectral and spatial features. Subsequently, [PERSON] et al. [21] proposed an innovative network design where attention weights are computed independently from spatial and spectral branches, followed by cross-branch multiplication of attention maps with original feature representations to facilitate mutual information exchange. The effect of these two transformer-based networks is better than that of the CNN-based method, but the feature interaction and fusion of PAN and MS are not sufficient when cross-modality fusion is performed [22]. There is still some degree of spectral distortion and loss of detail [21; 22]. To address the above challenges, we propose DCMFN. The network aims to build on the structure of the PanFormer network to further enhance and integrate the correlation and complementary information between