# Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping
## 4 Experiment and Analysis
### Experimental Settings
#### 4.1.3 Candidate Strips for Each Region  
The maximum power-up time of GF3 within one cycle is about two minutes. Therefore, the maximum flight distance of GF3 during one imaging does not exceed 912 km. For the five areas to be imaged in our experiment, the north-south distance is much greater than 912 km, so the satellite cannot continuously image when passing through each region. To reasonably obtain candidate imaging strips for each region, each region is decomposed based on the maximum flight distance of the GF3, as shown in Figure 6. Only one sub-region can be imaged each time the satellite passes, and the imaging time should not exceed  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Satellite** & **Gaofen-3** \\ \hline Launch Time & 10 August 2016 \\ Orbit Type & Repeat sun-synchronous orbit \\ Orbital Altitude (km) & 755 \\ Imaging Mode & Fine strip II \\ Swing Ability & 19–50\({}^{\circ}\) \\ Spatial Resolution (m) & 10 \\ Swath Width (km) & 100 \\ Band & C \\ Polarization & Dual polarization \\ \hline \hline \end{tabular}
\end{table}
Table 1: The parameters of Gaofen-3.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Region** & **Congo (K)** & **India** & **Australia** & **USA** & **Antarctica** \\ \hline Area (10,000 square km) & 234.5 & 298.0 & 774.1 & 936.4 & 1424.5 \\ Ranking in world & 11 & 7 & 6 & 4 & - \\ \hline \hline \end{tabular}
\end{table}
Table 2: Imaging area parameters.  
Figure 5: Distribution of imaging regions.  
the maximum. Due to its special geographical location, a circular decomposition was conducted in our experiment for Antarctica. Table 3 lists the number and acquisition time of candidate strips for each region.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Region**} & \multirow{2}{*}{**Sub-Region**} & \multirow{2}{*}{**Imaging Time**} & \multicolumn{2}{c}{**Number of Candidate Stripes**} & \multicolumn{2}{c}{**Number of Real/Binary Decision Variables**} \\  & & & **Candidate Stripes** & **Decision Variables** \\ \hline \multirow{4}{*}{Congo (K)} & 1 & 1 December 2019–31 December 2019 & 41 & \\  & 2 & 1 January 2020–31 January 2020 & 52 & 135 \\  & 3 & 1 February 2020–29 February 2020 & 42 & \\ \hline \multirow{4}{*}{India} & 1 & 1 December 2019–31 December 2019 & 37 & \\  & 2 & 1 January 2020–31 January 2020 & 80 & \\ \cline{1-1}  & 3 & 1 February 2020–29 February 2020 & 74 & 264 \\ \cline{1-1}  & 4 & 1 March 2020–31 March 2020 & 44 & \\ \cline{1-1}  & 5 & 1 April 2020–30 April 2020 & 29 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Candidate imaging strip information for each region.  
Figure 6: Region decomposition for obtaining candidate imaging strips. (**a**) Congo (K); (**b**) India; (**c**) Australia; (**d**) the United States; (**e**) Antarctica.  
After the screening, the imaging time window of each candidate strip and satellite position and the velocity coordinates within it are output. This paper does not list them one by one because of the large amount of data. According to the multi-objective imaging satellite task planning model, the number of candidate strips is the number of real or binary decision variables in ECL-INS-MOEA.