# Pre-Processing of Inner CCD Image Stitching of the SDGSAT-1 Satellite
## 4 Experimental Results  
Four multi-spectral images obtained from the SDGSAT-1 satellite are included to verify the performance of different line-time normalization methods. Figure 11 illustrates an overview of the tested image dataset acquired with different coverages. Line-time mismatching is one of the main error sources in image distortion. Traditionally, a designed line-time is used as the reference in image resampling. Slave images are resampled by interpolation of the designed line-time according to Equations (2)\(\sim\)(5), which changes evenly with time. In fact, the actual line-time is not completely coincident with the designed line-time, leading to different resampling intervals in object-space. Therefore, images resampled using the traditional line-time normalization method may share the problem of geometric dislocation in image space. Figures 12-15 show the stitching results of the four images collected from the SDGSAT-1 satellite in different scenes and coverage. Distinct views are selected from each image for the comparison of different line-time normalization methods. The inner CCD images in Figure 12a,e, Figure 13a,e, Figure 14a,e and Figure 15a,e illustrate the stitched results of eight different scenes. The normalized inner CCD images are resampled using the designed line-time and stitched directly. Located in the front part of a whole dataset, the misalignment is not obvious. With the accumulation of mismatched line-time errors, the stitching error can be easily detected, and distortions in these images are obvious, as in the examples shown in Figure 12a,e. As mentioned in Section 3.1, this image distortion is caused by the accumulation of mismatches between the actual and designed line-times. Figure 12b-d,f-h shows the results with line-based, block-based, and scene-based actual line-time normalization methods, respectively. Compared to the results obtained using the traditional designed line-time normalization method, these results show better geometric consistency between adjacent inner CCD images. The results in Figure 12d,h indicate the best performance among the alternatives, while the scene-based line-time normalization methods are not as stable. Figure 11: Cont. Figure 11: Illustration of detailed coverage of each dataset: (**a**) urban area in Image Data 1, (**b**) plains area in Image Data 2, (**c**) desert area in Image Data 3, (**d**) mountainous area in Image Data 4. Figure 12: Stitching results for Image Data 1 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. Figure 13: Stitching results for Image Data 2 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. Figure 14: Stitching results for Image Data 3 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.