## 3 Experiments
### Result and Discussion
Similarly, in order to determine the effect of the number of iterations on the experimental results, the number of iterations was increased to 100 for the experiments, and it was found that its minimum cost did not fluctuate significantly with the increase in the number of iterations, and its convergence algebra was almost the same as that of 50 iterations in Table 4. After the validation of several repetitions of the experiment, we found that the experiment was able to achieve the optimal balance between efficiency and solution quality when the iteration number is set to 50 and the initial population is set to 15. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Proportion** & **Pop** & \begin{tabular}{c} **EM Algorithm** \\ **Minimum Cost** \\ \end{tabular} & \
\begin{tabular}{c} **Tent Chaotic Map** \\ **Minimum Cost** \\ \end{tabular} &
\begin{tabular}{c} **Improved EM Algorithm** \\ **Minimum Cost** \\ \end{tabular} \\ \hline  
*325** & 317 & 304 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results (iteration = 50, population = 15; optimal solutions are marked in bold). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Proportion** & **Pop** & **Minimum Cost (Population = 15)** & **Minimum Cost (Population = 30)** \\ \hline
0.1 & 0.85 & 305 & 301 \\
0.2 & 0.85 & 305 & 300 \\
0.3 & 0.85 & 301 & 298 \\
0.5 & 0.85 & 300 & 301 \\
0.5 & 0.85 & 302 & 299 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Effect of population size on experimental results (iteration = 50). Based on the analysis of the above experimental results, we find that the improved EM algorithm proposed in this paper exhibits low sensitivity to these parameter adjustments, which highlights its robustness in the face of parameter changes. In other words, even if the parameters fluctuate within a certain range, the algorithm can still maintain a stable performance. In addition, the implementation of the algorithmic improvement strategy at each step of the process improves the performance of the algorithm, resulting in a more efficient final result compared to the previous one. In order to verify the feasibility and effectiveness of the improved EM algorithm proposed in this paper to solve the optimization screening problem of remote sensing  
Figure 5: Experimental results of initial population construction using EM algorithm and tent chaotic map. Figure 6: Experiment with 3D surface map (corresponding to Experiment BCD from top to bottom). data, we use the datasets of Hebei and Inner Mongolia for screening. We summarize the results obtained by using other meta-heuristic algorithms for data screening in Experiment E in Table 6, and through comparative analysis, we find that there are differences in the data screening effects of different algorithms for different regions, which may be related to the characteristics of regional data and the design of the algorithms. In each region, the improved EM algorithm screened the least number of scenes, followed by PSO and greedy algorithm, and the GA and ACO had poor screening results. Because the greedy algorithm is representative in the field of remote sensing image screening, we use the greedy algorithm as a benchmark method, and we find that the improved EM algorithm improves its optimal solution by about 9.78% on average compared to the greedy algorithm. In order to verify that the improvement was not caused by randomization, we conducted a paired sample t-test and confidence intervals. The original hypothesis (H0) was that there is no significant difference between the screening results of the two methods (mean difference is 0), and the alternative hypothesis (H1) is that there is a significant difference between the screening results of the two methods (mean difference is not 0). The result \(p\)-value = 0.0287 \(<\) 0.05 was obtained, then, the original hypothesis was rejected and the two methods were considered to show a significant difference in the screening results.