## 5 Discussion  
Although microsatellites have the advantages of compactness, low cost, and flexibility, their flight attitude may be unstable occasionally and the equipment measuring the attitude and position may be inaccurate or perform poorly, which leads to large geolocation errors in the images [35]. For example, the geolocation errors in UNIFORM-1\({}^{\prime}\)s visible images are 50-100 km [35]. The BNU-1 L1A product has smaller geolocation errors, but the average geolocation error can still be up to 10 km, which is close to that of the Luojia 1-01 data [30]. The non-parametric geometric correction methods are widely applied to geolocation correction of images without distinguishing the error sources [16; 31], such as HJ-1A/B CCD images [36] and Unmanned Aerial Vehicle (UAV) images [37]. The geolocation accuracy of these geometric correction methods relies mainly on adequate CPs. However, the limited texture features of the ice and snow surface in polar images make it difficult to extract the CPs. Some studies prove that image division and image enhancement have the ability to increase the amount of extracted CPs [1; 21; 22; 34]. However, images used by these previous studies are from low- and mid- latitudes and contain rich land surface features. The correction for polar images with few texture features is rarely documented. This study proposes the geometric correction method to reduce the geolocation errors of the visible images for polar regions. The results indicate that piecewise linear enhancement highlights more surface features of ice and snow surfaces than other image enhancement methods. Some other studies have also proved that piecewise linear enhancement is effective in highlighting more texture features of the ice and snow surfaces in polar images [34]. More CPs can be observed after the image pair is processed by image division and piecewise linear enhancement. Different division schemes can be adopted to obtain more CPs for different image pairs. In addition, we compared the geolocation accuracy of Sample Image A-F after the correction through the CPs extracted from the original image and the geolocation accuracy after correction through the optimal CPs extraction scheme (Table 3). It was found that the geolocation accuracy of Sample Image A-E was not significantly improved. The geolocation accuracy of Sample Image A-E after geometric correction based on the CPs extracted from the original image was close to the level of 250 m (the pixel size of MODIS image), and it was difficult to further improve by adding CPs on this basis. However, for some images where the ice sheet is widely distributed, such as Sample Image F, the proposed method effectively prevents the distortion of the corrected image caused by the lack of CPs on the ice sheet by adding CPs. The increase in CPs can remarkably improve the geolocation accuracy of such images. Therefore, the automatic geometric correction method proposed in this study is of great significance for the correction of images in polar regions with rare feature points. Although the method presented in this study has some advantages in correcting the geolocation errors of polar images, it also has its limitations. For example, only two division \begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{3}{*}{**Sample Image**} & \multicolumn{3}{c}{**Geolocation Accuracy of the Corrected**} \\  & \multicolumn{2}{c}{**BNU-1 Image (m)**} & \multirow{2}{*}{**Improvement in**} \\  & **Corrected by the CPs** & & **Geolocation** \\  & **Extracted from the** & & **Extracted from the** \\  & **Original Image** & **Optimal Scheme** & \\ \hline A & 279.42 & 270.19 & 3.30 \\ B & 260.80 & 253.40 & 2.84 \\ C & 275.79 & 269.83 & 2.16 \\ D & 305.28 & 302.29 & 0.98 \\ E & 242.43 & 221.59 & 8.60 \\ F & /(Serious distortion) & 783.90 & / \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of the geolocation accuracy of the BNU-1 images corrected through different CPs extraction Scheme.