# Pre-Processing of Inner CCD Image Stitching of the SDGSAT-1 Satellite
## 5 Discussion and Conclusions  
Generally, inner CCD image stitching methods can be divided into two categories: image-space based methods and object-space based methods. Object-space methods are based on a pseudo-rigorous imaging model applied to the stitched imagery. First, the geolocation parameters (interior and exterior) of each inner CCD image should be calibrated to achieve relative geolocation orientation. Tie points extracted by image matching algorithms are utilized to develop the transformation model. To simplify the relationships between adjacent CCD images, two-dimensional affine transformation models are based on the assumption of limited geometric distortions. After that, a pseudo-geolocation model is developed by fitting the geometric relationships of the stitched image and each inner CCD image. Image-space pixels are projected to the object-space coordinates,and ground information is sometimes included to guarantee the geolocation accuracy. The parameters of the pseudo-rigorous model are fitted using the projected points, and the inner CCD image-space coordinates can be reprojected into the stitched images. In contrast, image-space methods define transformation models between adjacent inner CCD images to fit the geometric relationship of the corresponding image coordinates. Usually, an affine transformation model is utilized with line-time normalization. Based on the designed line-time of the imaging system, the imaging period is divided into several parts based on the assumption of a uniform sampling rate. Nowadays, most researchers are interested in the development of object-space methods, as image-space methods based on the designed line-time lead to geometric distortions that are hard to calibrated. As for the object-space methods, the developed pseudo-rigorous model in object-space can be directly applied for subsequent geometric processing of the stitched products, and the final stitching results rely on the quality of image-space normalization. However, there are three main limitations of object-space methods: (1) the \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{\begin{tabular}{c} **Traditional** \\ **Line-Time** \\ **Normalization** \\ \end{tabular} } & \multicolumn{2}{c}{\begin{tabular}{c} **Line-Based** \\ **Line-Time** \\ **Normalization** \\ \end{tabular} } & \multicolumn{2}{c}{\
\begin{tabular}{c} **Block-Based** \\ **Line-Time** \\ **Normalization** \\ \end{tabular} } & \multicolumn{2}{c}{
\begin{tabular}{c} **Scene-Based** \\ **Line-Time** \\ **Normalization** \\ \end{tabular} } \\ \hline Image Data 1 & 5.60 & 4.72 & 5.22 & 56.67 \\ \hline Image Data 2 & 5.42 & 4.97 & 5.63 & 59.74 \\ \hline Image Data 3 & 5.33 & 5.03 & 5.41 & 60.53 \\ \hline Image Data 4 & 5.68 & 4.88 & 5.27 & 58.47 \\ \hline Average & 5.51 & 4.90 & 5.38 & 58.85 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Time consumption of different stitching methods (seconds). \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Methods**} & \multicolumn{2}{c}{ \begin{tabular}{c} **Traditional** \\ **Line-Time** \\ **Normalization** \\ \end{tabular}  } & \multicolumn{2}{c}{ \begin{tabular}{c} **Line-Based** \\ **Line-Time** \\ **Normalization** \\ \end{tabular}  } & \multicolumn{2}{c}{\ \begin{tabular}{c} **Block-Based** \\ **Line-Time** \\ **Normalization** \\ \end{tabular}  } & \multicolumn{2}{c}{ \begin{tabular}{c} **Scene-Based** \\ **Line-Time** \\ **Normalization** \\ \end{tabular}