## 6 Discussion  
In this paper, we examine the importance and potential benefits of combining pan-sharpening technology with remote sensing imagery within the realms of ecological and environmental monitoring. The DCMFN method introduced herein has shown superior performance compared to existing deep learning approaches, as evidenced by ablation studies and comparative analyses conducted on three distinct datasets, which are shown in Tables 6-11. Our model is designed to surmount several pivotal challenges in ecological monitoring. First and foremost, the satellite imagery prevalently employed in ecological research often has limited spatial resolution. In ecological investigations, high-resolution data play an irreplaceable role in accurately discerning and charting diverse ecological elements. For instance, they are essential for pinpointing individual plant species, delineating small-scale habitats, and identifying wildlife corridors. The pansharpening techniques integrated into our model serve to augment the spatial details within multispectral images. This augmentation enables us to monitor these ecological features with far greater precision, facilitating more in-depth ecological studies. Another formidable challenge stems from the intricate and ever-changing nature of ecological systems. Ecological variables are in a constant state of flux over time. Traditional monitoring approaches frequently struggle with capturing these rapid and often subtle shifts. However, our model, equipped with the ability to promptly process and analyze pansharpened data, demonstrates remarkable sensitivity in detecting minute changes in aspects such as vegetation vitality, water quality, and land cover. This capability is invaluable for the early detection of ecological disruptions. Whether it is the encroachment of invasive species, the onset of deforestation, or the degradation of wetlands, our model can sound the alarm at an early stage, enabling timely intervention.In addition, the cost effectiveness of ecological monitoring is a critical consideration, particularly for large-scale monitoring initiatives. The acquisition of high-resolution imagery typically incurs substantial costs. Our model offers a more economically viable solution by leveraging pansharpening to enhance the quality of existing lower-resolution data. In doing so, it extracts the maximum value from the available data resources. This not only reduces the need for frequent and costly high-resolution satellite data acquisitions but also ensures that the requisite level of detail for effective ecological monitoring is maintained. By striking this balance, our model makes significant contributions to the sustainable development of ecological monitoring efforts. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **\#Params** & **Times** \\ \hline PNN & 0.080 M & 0.0012 \\ \hline PanNet & 0.078 M & 0.0032 \\ \hline MSDCNN & 0.262 M & 0.0035 \\ \hline TFNET & 2.359 M & 0.0095 \\ \hline GGPCRN & 1.771 M & 0.014 \\ \hline MUCNN & 1.36 M & 0.0080 \\ \hline PanFormer & 1.530 M & 0.0468 \\ \hline SSIN & 3.36 M & 0.0157 \\ \hline DCMFN & 1.710 M & 0.0512 \\ \hline \hline \end{tabular}
\end{table} 
Table 12: Inference time and parameters of deep learning-based models. Nevertheless, in Figures 10-15, we can see that in both RR and FR, although the performance of our DCMFN is excellent, there is still room for improvement. For example, in our experiments, we observe that the DCMFN method struggles under specific conditions. In complex scenes such as highly urbanized areas with numerous high-rises and intricate road networks, the method fails to fully capture and reconstruct details, leading to blurry pansharpening results. When dealing with low-SNR input data, the method has difficulty differentiating real details from noise, causing inaccurate sharpening and introducing artifacts. Pansharpening significantly boosts the resolution of remote sensing images, allowing for a more accurate depiction of subtle changes on Earth's surface.