# Pre-Processing of Inner CCD Image Stitching of the SDGSAT-1 Satellite
## 1 Introduction
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & & Num of CCDs & 8 \\ \cline{3-4}  & Multispectral & Ground Resolution & 10 m \\ \cline{3-4}  & Sensor & Num of Channels & 7 \\ \cline{3-4}  & & Imaging Type & Pushbroom \\ \cline{3-4}  & & Num of CCDs & 8 \\ \cline{3-4}  & & Num of Channels & 10/40 m \\ \cline{3-4}  & & Num of Channels & 5 \\ \cline{3-4}  & & Imaging Type & Pushbroom \\ \cline{3-4}  & & Num of CCDs & 4 \\ \cline{3-4}  & & Ground Resolution & 30 m \\ \cline{3-4}  & & Num of Channels & 3 \\ \cline{3-4}  & & Imaging Type & Whiskbroom \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Designed characteristics of the SDGSAT-1 satellite. Models (DEMs); other investigations have come to the same conclusion using a variety of experimental datasets [10; 11]. To obtain stitched images with satisfactory geometric performance, ref. [12] applied a simple translation model for the calibration of stitched images from the IRS-1C satellite. Considering only three inner CCDs assembled on the focal plane, the stitching accuracy reached the sub-pixel level. However, much higher accuracy is not expected for a precise affine transformation model using this dataset [13]. In [14], the authors evaluated the performance of stitched images obtained from multiple CCD sensors on the IKONOS satellite. A pseudo-orientation model was proposed for the calibration of standard level 1 products, and the suitability of the rational function model (RFM) was investigated as well. The authors of [15] proposed a generic model for the calibration of stitched imagery from the ALOS/PRISM satellite. Due to insufficient calibration of the joint error between inner CCD images, systematic errors could not be completely corrected. In [16], the authors analyzed the geometric performance of TianHui-1 (TH-1) satellite imagery and proposed an Equivalent Frame Photo (EFP) model for the calibration of line-matrix CCD images. They were able to improve the accuracy of the TH-1 imagery from about 170 m to 11.8 m. With the aim of producing a stitched lunar map, [17] proposed an automatic stitching method for 2C level data based on an image registration and geolocation model. In [18; 19], the authors proposed an improved geometric calibration model for the production of stitched images using datasets from the HaiYang-1C satellite. The authors of [20] proposed an integral imaging model based on inter-chip geometry constraints. Table 2 summarizes the main characteristics of traditional multiple CCD image stitching methods, which are divided into two categories: object-space methods and image-space methods. A pseudo-rigorous model is necessary to build the relationship between inner CCD images and stitched images for object-space methods, while image-space methods depend on the normalization of imaging sampling intervals for each inner CCD image. In 2009, ref. [21] systematically analyzed the error sources of stitched multiple CCD images from both the along-track direction and the cross-track direction. An object-space correction method was proposed for the calibration of multiple CCD images. Later, [22] proposed a rigorous geometric sensor model for multiple CCD image stitching. By separating each CCD image, a rigorous sensor model with scaling, rotation, and translation parameters was established for the resampling of stitched images. Similarly, [3] proposed an object-space reprojection model to generate a stitched image by indirect geometric image rectification  
Figure 1: Focal plane arrangement of different satellite platforms: (**a**) IKONOS imaging plane, (**b**) WorldView-2 imaging plane, (**c**) Pleiades imaging plane(Letters A E is the index of inner CCD sensors), (**d**) ZY-3 imaging plane. using ZY-1 satellite images, and [23] used penalized splines to model the inner CCD distortions of WorldView-3 imagery. A pseudo-geolocation model is necessary to build the relationship between the inner CCD images and the stitched products; however, extra fitting errors are introduced during reprojection, and computational complexity is relatively high.