## 4 Experiments  
### Datasets  
Pansharpening often lacks reference images (Ground Truth, GT) in practical applications, complicating the assessment of the pansharpened image quality. To tackle this issue, [PERSON] et al. introduced an innovative methodology for synthesizing training datasets using the Wald protocol [10]. This methodology generates a dataset encompassing input MS images, PAN images, and HRMS images. The initial PAN and MS images are first processed into blurred versions, employing a modulation transfer function (MTF) kernel for this purpose. After the initial step, the PAN and MS images undergo a reduction in resolution by dividing their pixel dimensions by four. Meanwhile, the original MS image is used as the GT for supervised learning. Currently, most pansharpening algorithms that utilize supervised deep learning generate datasets following this methodology. On the one hand, they employ reference image quality evaluation metrics to assess fusion results at a reduced resolution. On the other hand, non-reference image quality metrics are used to evaluate fusion results at full resolution. By considering experimental results at not only full resolution (FR) but also reduced resolution (RR), the quality of fusion images can be assessed more comprehensively. To clarify the application of RR and FR datasets in training and testing, the flowchart illustrating the training and testing phases using the supervised learning method is presented in Figure 8.  
The study presented in [24] offers multiple datasets for pansharpening evaluation. To evaluate the effectiveness of DCMFN, we performed a series of experiments. These experiments were conducted on three urban remote sensing datasets: QuickBird (QB), WorldView-2 (WV2), and WorldView-4 (WV4). The goal was to comprehensively evaluate the performance of the DCMFN across these datasets. These datasets were sourced from commercial satellite systems operated by specialized companies, with a primary focus on urban environments, particularly buildings, while also encompassing other features such as trees and roads [13]. the The datasets used in this study (QuickBird, WorldView-2, WorldView-4) are publicly available for research purposes. They can be accessed through the DigitalGlobe Open Data Program ([[https://www.digitalglobe.com/](https://www.digitalglobe.com/)]([https://www.digitalglobe.com/](https://www.digitalglobe.com/))). Detailed information is provided in Table 3.  
[PERSON] et al. [25] divided the QuickBird (QB) dataset into six categories when used to observe land cover distribution: Class 1: Paddy field; Class 2: Evergreen forest; Class 3: Deciduous forest; Class 4: Cropland; Class 5: Urban area; Class 6: Bare land. These six categories allow for a better understanding of land cover types in different ecological areas, which in turn facilitates research in the following aspects: urban planning (such as analyzing urban structure and infrastructure development through high-resolution images); land use/land cover change (e.g., monitoring and analyzing land changes, assessing deforestation, agricultural expansion, and urbanization); environmental monitoring (e.g., ecosystem health assessment, pollution monitoring, and water pollution analysis); post-disaster assessment (e.g., assessing damage, infrastructure destruction, and more after natural disasters like floods, earthquakes, and fires); and agricultural monitoring (e.g., investigating crop growth, soil conditions, irrigation, and other agricultural factors). As for the Worldview-2 dataset (WV2), [PERSON] et al. mentioned in [26], their study has the potential to combine Worldview-2 data with selected environmental variables to estimate and map above-ground biomass and carbon stocks of managed plantations in a watershed. Therefore, we selected this dataset to study the impact of DCMFN on the ecological environment. [PERSON] et al. proposed that the use of a high spatial resolution drone and satellite  
Figure 8: The application of full and reduced resolution datasets in training and testing.