## 4 Experimental Section
### Datasets  
In this study, the BigEarthNet-S2 dataset was used for the color correction of high-resolution remote sensing images. The dataset consists of 115 Sentinel-2 tiles collected from June 2017 to May 2018 across 10 European countries (Austria, Belgium, Finland, Ireland, Kosovo, Latvia, Luxembourg, Portugal, Serbia, and Switzerland). These tiles were atmosprically corrected using the Sentinel-2 Level 2A product creation and formatting tool and were then divided into a total of 549,488 image patches.  
Each image patch is associated with a pixel-level reference map and multiple land cover class labels (i.e., multi-label) derived using the latest 2018 CORINE Land Coverdatabase. The dataset includes a wide range of terrains, seasons, and weather conditions, making it sufficient for training color correction models in various environments.  
The BigEarthNet-S2 dataset utilizes imagery collected by the Sentinel-2 satellite, which is part of the European Space Agency's (ESA's) Copernicus project. Sentinel-2 provides a wide range of spectral bands for precise observations of the Earth's surface, and the cloud cover in all Sentinel-2 images is limited to 1% or less to minimize its impact on the results. The spectral bands available in the Sentinel-2 imagery include the following: B01 (coastal aerosol; 60 m), B02 (blue; 10 m), B03 (green; 10 m), B04 (red; 10 m), B05 (vegetation red edge; 20 m), B06 (vegetation red edge; 20 m), B07 (vegetation red edge; 20 m), B08 (near-infrared (NIR); 10 m), B09 (water vapor; 60 m), B11 (short-wave infrared (SWIR); 20 m), B12 (SWIR; 20 m), and B8A (narrow NIR; 20 m). Among these, bands B04, B03, and B02, which correspond to red, green, and blue, were selected to create the RGB image. The choice was made due to their 10 m resolution, which offers an optimal balance between spatial detail and accurate color representation for natural imagery. In this study, a high-resolution RGB image created by combining the red, green, and blue channels (bands 4, 3, and 2) was used as the reference image, as shown in Figure 3.  
To create the target image, the RGB image was randomly divided in one of the following ways: vertical bisection, diagonal division, three divisions, or four divisions, and then randomly color-adjusted, luminance-adjusted, and contrast-adjusted. Color adjustment was performed by selecting a random value between \(-\)30 and +30 to correct the hue and change the ratio of primary colors; luminance adjustment was performed by selecting a random value between \(-\)50 and +50 to adjust the overall brightness; and contrast adjustment was performed by selecting a random value between 0.5 and 1.5 to adjust the contrast, as shown in Figure 4. These parameter ranges were determined based on empirical analysis. The chosen values were optimized to provide sufficient variability while maintaining the natural appearance of terrain images, ensuring that the adjustments align with the real-world conditions observed in remote sensing environments. The RGB image and the preprocessed image were then resized to 256 \(\times\) 256 pixels and normalized to the range [0, 1]. The RGB image and the preprocessed target image were then paired and used for training.