## 4 Conclusions  
In this paper, we first transform the remote sensing data optimization screening problem into solving the SCP, and secondly, apply the EM algorithm to the SCP, and then, improve the EM algorithm. The improvement idea is as follows:  
1. Using tent chaotic mapping to construct the initial population so that it can be more uniformly distributed in the feasible domains;
2. Combining with a new local search strategy and employ an additional heuristic operator to generate feasible solutions to avoid the algorithm from falling into a local optimum prematurely;
3. Normalizing the objective function value to simplify the charge calculation formula, and introducing the idea of variation strategy in differential evolutionary algorithms to simplify the resultant force calculation formula, which significantly reduces the amount of calculation;
4. Introducing the idea of differential evolutionary algorithms in moving particle process, which improves the convergence speed of the algorithm.  
To verify the effectiveness of the improved EM algorithm proposed in this paper, we utilize the U.S., Hebei, and Inner Mongolia datasets to conduct experiments. The experimental results show that the improved EM algorithm gives better results, searches the global optimal solution faster, and has better robustness. Taking the dataset discussed in this paper as an example, compared with the greedy algorithm, the optimal solution obtained in this study has been improved by 9.78% on average. This achievement has been successfully applied to data optimization scenarios in practical work with remarkable effectiveness, which not only effectively reduces the cost of data storage, transmission, and processing, but also practically improves the work efficiency. Moreover, it strongly supports the data services of various remote sensing applications such as urban and rural planning [24], natural disaster and environmental monitoring [25], and land resource surveys [26], thereby enhancing the utilization efficiency and application value of data.  
Conceptualization, Y.F. and K.L.; methodology, Y.F. and K.L.; software, Y.F.; validation, H.W. and L.D.; formal analysis, X.Z. and G.L.; data curation, K.L.; writing--original draft preparation, Y.F.; writing--review and editing, K.L. and X.Z.; project administration, S.L.; funding acquisition, K.L. and G.L. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the National Key Research and Development Program of China (2023 YFE0208000 to K.L.), the National Natural Science Foundation of China (42202280 to G.L.), the National Natural Science Foundation of China (72242106 to X.Z.), Project of Social Science Foundation of Xinjiang Uygur Autonomous Region (2023 BTY128 to X.Z.), Project of Natural Science Foundation of Xinjiang Uygur Autonomous Region(2023D01A57 to X.Z.), Beijing Social Science Foundation(24 YTC038 to X.Z.).  
Not applicable.  
All relevant data required to reproduce the analysis in the paper are hosted at the Github repository \"remote_sensing\", available at the link: [[https://github.com/FANGYINGJ/remote_sensing](https://github.com/FANGYINGJ/remote_sensing)]([https://github.com/FANGYINGJ/remote_sensing](https://github.com/FANGYINGJ/remote_sensing)) (accessed on 30 November 2024).  
The authors declare no conflicts of interest.