## 4 Results
### Influence of Image Division and Enhancement on the CPs Extraction  
Sample Image E is a typical image for polar regions. Most of the features in the image are ice sheets and snow with limited boundary features, and only a few of them are sea ice with well-defined boundaries. However, due to the high reflectance of ice and snow surfaces in polar regions, the textures of the ice sheet and snow can rarely be observed in images. The ice sheet area of Sample Image E is a good case for evaluating the effectiveness of various image enhancement methods for increasing the control points on the ice sheet. Five image enhancement methods, which are linear enhancement, piecewise linear enhancement, Gaussian enhancement, equalization enhancement, and square root enhancement, were applied to enhance Sample Image E. Figure 6 shows the distributions of CPs extracted from the images enhanced by different image enhancement methods. The numbers of CPs extracted from the original image and the image stretched by the five enhancement methods were 31, 32, 76, 37, 27, and 22, respectively. By comparing these five enhancement methods, we found that the piecewise linear enhancement method makes the surface textures in the interior of the ice sheet more distinct, and as a result, the most CPs were extracted from the image. Therefore, the piecewise linear enhancement (Figure 6c) is considered to be more suitable for enhancing the images of polar regions. Figure 5: Three-dimensional scatter diagram of the influence of the satellite’s attitude angle change on the geolocation change. (**a**,**b**) show the impact of the satellite’s attitude changes on the longitude change and the latitude change of the upper left corner point of the image, respectively. (**c**,**d**) show the impact of the satellite’s attitude changes on the longitude change and the latitude change of the center point of the image, respectively. The color of the scatters represents the longitude/latitude change according to the legend. Sample Image E was also used to assess the influence of image division on CPs extraction. The image was divided into four sub-images (Scheme 1) and nine sub-images (Scheme 2) and then each sub-image was individually enhanced with the piecewise linear stretching method. Figure 7 shows the distribution of CPs extracted from the original image by Scheme 1, and by Scheme 2, respectively. The number of extracted points are 245, 435, and 596, respectively. More CPs are extracted in the center and the lower right corner of the image (the blue border area) divided by Scheme 2 (Figure 7c) compared to the original image (Figure 7a) and the image divided by Scheme 1 (Figure 7b). As shown in Table 2, the amount of CPs extracted from the image increases by 30% to 182% when the image division and piecewise linear enhancement were applied to the images. However, this does not mean we can get more CPs if the image is divided into more sub-images. The amount of CPs extracted from the Sample Image A, B, C, and D is less when Scheme 2 is applied to divide these images. The CPs extracted from Sample Image E were used to correct the geolocation errors of the image. The BNU L1A/L1B image with 50% transparency is superimposed on the corresponding MODIS image in Figure 8. There is a distinct displacement between the BNU-1 L1A image and the MODIS image, while the displacement between the BNU-1 L1B image and the MODIS image can barely be discerned. This result indicates that the geolocation correction method improves the geolocation accuracy of the BNU-1 L1 Aproduct. Figure 6: Comparisons of the number and distribution of CPs in the original image (**a**) and the image adopting different enhancement methods: (**b**) Linear; (**c**) Piecewise Liner; (**d**) Gaussian; (**e**) Equalization; (**f**) Square Root. In addition to Sample Image E, Sample Image F was also selected to evaluate the effectiveness of the CPs extraction scheme proposed. Most of the areas of Sample Image F are covered by the ice sheet, and only a few areas are fjords.