# Pre-Processing of Inner CCD Image Stitching of the SDGSAT-1 Satellite
## 4 Experimental Results
Figure 15: Stitching results for Image Data 4 with different methods: (**a**) traditional line-time normalization, (**b**) line-based line-time normalization, (**c**) block-based line-time normalization, (**d**) scene-based line-time normalization, (**e**) traditional line-time normalization, (**f**) line-based line-time normalization, (**g**) block-based line-time normalization, (**h**) scene-based line-time normalization. As can be seen in all subfigures shown in Figures 13 and 15, the investigated actual line-time normalization methods can achieve results as good as the traditional line-time normalization method. The accumulated difference between the actual and designed line-time is not noticeable. Therefore, the stitching error between adjacent sub-CCD images is usually at the sub-pixel level regardless of the method. For the results in Figure 14b-d,e-h, pixel-level stitching errors can be detected when using the scene-based line-time normalization method. Usually, a whole dataset is separated into different scenes according to the design of standard products. For the scene-based line-time normalization method, the recorded line-time is considered as a constant in the imaging period of a scene. In Figure 14d,h the discrepancy is obvious due to the instability of the actual line-time. However, the actual line-time changes line by line and is influenced by various factors, meaning that additional errors are introduced during the fitting process. In contrast, the actual line-time is considered unchangeable in each block, and an average resampling rate is used as a replacement for the resampling of each inner CCD image. Better results are obtained when using the block-based line-time normalization method, as shown in Figure 14c,g. In the block-based line-time normalization method, scan lines located at step-points are recorded as the boundary of each block and the normalized image of each scanline is fitted using a linear transformation. Therefore, the block-based normalization results can reduce the inconsistencies between different sub-CCD images. Traditional designed line-time normalization, scene-based line-time normalization, and block-based line-time normalization can be regarded as either global or local fitting of the actual line-time normalization. These methods introduce distortions caused by the misalignment of the normalized line-time and the actual line-time, which influences the geometric performance of standard products. Therefore, a line-based actual line-time normalization method is considered. In this experiment, the obtained inner CCD image located at the edge of each camera is selected as the reference image and all slave images are resampled using the interpolation of the actual line-time of each inner CCD image. The resampled image is reproduced using Equations (6)\(\sim\)(8). The results in Figure 14d-h illustrate that no obvious dislocations between adjacent sub-CCD imagery can be identified. In total, the results of the line-based line-time normalization method achieve the best stitching accuracy of the different methods. Table 3 illustrates the root mean square error (RMSE) of the stitching error between adjacent sub-CCD images using different line-time normalization methods, where \(X\), \(Y\), and \(P\) represent the error in the line direction, sample direction, and plane direction in image-space, respectively. After the calibration of the systematic error, tie points are extracted for the verification of stitching accuracy. The stitching accuracy of all experimented datasets processed based on the traditional line-time normalization method is around 0.8 pixels, while the results obtained using the other three methods are around 0.5 pixels. The line-based line-time normalization method achieves the best accuracy, with better than 0.5 pixels for all scenes. Figure 16a-d shows the statistical results of the stitching error with different methods. The blue line is the traditional normalization method based on designed line-time, and changes a great deal with eight adjacent sub-CCD images.