## 3 Experiments
### Datasets and Preprocessing
#### 3.1.1 Real-World Multi-Sensor LR-HR Dataset: GF_Sen  
To enhance the network's generalization on sensor and spatiotemporal dimensions, this paper constructs a set of paired real-world multi-sensor LR-HR data, in which Sentinel-2 data with 10 m spatial resolution are used as the LR image, and GaoFen-2 data with 4 m spatial resolution are used as the HR images. This dataset is referred to as GF_Sen, and the details of the dataset are shown in Table 2. The HR images are downsampled to generate LR images as simulated data.  
To build a paired dataset for SR, the two sensors need to image the same ground targets at the same positions in the electromagnetic spectrum. In this study, we finally chose three bands of blue, green, and red. The study area includes four different cities in Guangdong Province, each with diverse land cover and ecological types. Some sample images of the dataset are shown in Figure 3.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Location** & **Sensor** & **Filename** \\ \hline Guangzhou & Sentinel-2 & L1C\_T49 QGF\_A003347 20191002T030635 \\ Guangzhou & GaoFen-2 & GF2\_PMS1\_E113\_N2\_N23\_20191227\_L1A0004507303 \\ Guangzhou & Sentinel-2 & L1C\_T49 QGF\_A003347\_20191002T030635 \\ Guangzhou & GaoFen-2 & GF2\_PMS2\_E113\_A23\_20191227\_L1A0004505416 \\ Shenzhen & Sentinel-2 & L1C\_T49 QGF\_A003347\_20170102T030705 \\ Shenzhen & GaoFen-2 & GF2\_PMS1\_E113\_8\_N2\_8\_20171227\_L1A0002883454 \\ Shenzhen & Sentinel-2 & L1C\_T49 QGF\_A003347\_20170102T030705 \\ Shenzhen & GaoFen-2 & GF2\_PMS2\_E114\_0\_N2\_26\_20171227\_L1A0002883537 \\ Dongguan & Sentinel-2 & L1C\_T49 QHF\_A003347\_20171030705 \\ Dongguan & GaoFen-2 & GF2\_PMS2\_E114\_1\_N2\_9\_20171227\_L1A0002883531 \\ Huizhou & Sentinel-2 & L1C\_T50 QKL\_A007994\_20170102T025445 \\ Huizhou & GaoFen-2 & GF2\_PMS1\_E114\_5\_N23\_1_20161128\_L1A0001994661 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The information of used data.  
Figure 3: LR-HR image-pair samples in GF_Sen dataset. The first row is HR images from GaoFen-2, while the second row is the corresponding LR images from Sentinel-2.  
Although searching different satellite data within a small time window can reduce variations caused by atmospheric conditions and environmental changes, only a limited number of images meet such strict criteria. Therefore, we expanded the time window to 3 months. After observation, there were basically only a few active objects (such as ships and vehicles) exhibiting differences in this period. We searched the data of the study area (as shown in Figure 4), selected the images with less than 5% of the total cloud cover, and finally deleted the image data samples with clouds. After resampling the GaoFen-2 data using a bilinear sampling method to a 5 m resolution, we registered the Sentinel-2 data with reference to the GaoFen-2 data.  
Following the preprocessing of the images mentioned above, the HR images were cropped to a size of 128 \(\times\) 128, while the LR images were cropped to a size of 64 \(\times\) 64. The GF_Sen dataset was finally created, consisting of 5263 images for the training set, 2255 images for the validation set, and 3930 images for the test set.