## 4 Atmospheric Correction Results and Analysis  
### Comparison of Images Before and After Atmospheric Correction  
The atmospheric correction algorithm described in Section 3 effectively addresses atmospheric scattering, adjacency effects, and BRDF-related distortions. Using atmospheric parameters retrieved synchronously by the PSAC, atmospheric correction was applied to wide-swath, wide-field-of-view multispectral images. The corrected images encompass various land cover types, including urban areas, vegetation, water bodies, and soils. Figures 4-6 display some of the atmospheric correction results. Figures 4a, 5a and 6a show the images before atmospheric correction, while Figures 4b, 5b and 6b show the images after atmospheric correction, with both presented as color composites using the first three bands of the MSC. Figure 4: Comparison of pre- and post-atmospheric correction for HJ-2A satellite multispectral image of Beijing Daxing Airport, China. The red-marked and green-marked areas represent the selected regions for comparison and validation with Sentinel-2 data, as described in Section 4.3. (CCD1, 14 November 2022; AOD = 0.446; CWV = 0.51 g/cm\({}^{2}\)). (**a**) Before atmospheric correction. (**b**) After atmospheric correction. Figure 5: Comparison of pre- and post-atmospheric correction for an H)-2B satellite multispectral image of the Indian Plains region. The red-marked and green-marked areas represent the selected regions for comparison and validation with Sentinel-2 data, as described in Section 4.3. (CCD3, 25 November 2022; AOD = 0.208; CWV = 0.96 g/cm\({}^{2}\)). **(a)** Before atmospheric correction. **(b)** After atmospheric correction. Figure 6: Comparison of pre- and post-atmospheric correction for H)-2A satellite multispectral image of Xianning City, Hubei Province, China. The red-marked and green-marked areas represent the selected regions for comparison and validation with Sentinel-2 data, as described in Section 4.3. (CCD3, 23 December 2022; AOD = 0.564; CWV = 0.45 g/cm\({}^{2}\)). **(a)** Before atmospheric correction. **(b)** After atmospheric correction. Figure 4 shows the atmospheric correction results for Beijing Airport, captured by the HJ-2A satellite's MSC (CCD1). Before atmospheric correction, the image exhibits low contrast in land features, with blurred boundaries of the airport runway and buildings. After atmospheric correction, the boundaries of the airport runway and buildings become clear. Figure 5 presents a comparison of the atmospheric correction results for the Indian plains, captured by the HJ-2B satellite's MSC (CCD3). The corrected image is notably clearer, with the contours of the rivers being more distinctly visible. Figure 6 displays the comparison of atmospheric correction results for a mountainous urban area, captured by the HJ-2A satellite's MSC (CCD3). Compared to the pre-correction image, the corrected image shows more natural and vibrant colors, with clearer texture details. These indicate that atmospheric correction effectively removes the blurring effects caused by atmospheric interference, thereby improving the recognition of land features. To quantitatively evaluate the improvement in image quality after atmospheric correction, two metrics--contrast and clarity--were calculated for the images before and after correction. (a) Contrast  
Contrast represents the ratio between bright and dark regions in an image, reflecting the gradient transition from light to dark. A higher contrast value indicates more gradient levels and richer image details [25; 26]. Contrast is calculated as follows:  
\[C=\sqrt{\frac{1}{\sum_{i=1}^{N}w_{i}}{\sum_{i=1}^{N}w_{i}}(\frac{DN_{i}-DN)^{2 }}{DN^{2}}} \tag{9}\]  
where \(N\) represents the number of pixels in the local region of the image to be evaluated.