# Large-Scale Multi-Objective Imaging Satellite Task Planning Algorithm for Vast Area Mapping
###### Abstract  
With satellite quantity and quality development in recent years, remote sensing products in vast areas are becoming widely used in more and more fields. The acquisition of large regional images requires the scientific and efficient utilization of satellite resources through imaging satellite task planning technology. However, for imaging satellite task planning in a vast area, a large number of decision variables are introduced into the imaging satellite task planning model, making it difficult for existing optimization algorithms to obtain reliable solutions. This is because the search space of the solution increases the exponential growth with the increase in the number of decision variables, which causes the search performance of optimization algorithms to decrease significantly. This paper proposes a large-scale multi-objective optimization algorithm based on efficient competition learning and improved non-dominated sorting (ECL-INS-LMOA) to efficiently obtain satellite imaging schemes for large areas. ECL-INS-LMOA adopted the idea of two-stage evolution to meet the different needs in different evolutionary stages. In the early stage, the proposed efficient competitive learning particle update strategy (ECLUS) and the improved NSGA-II were run alternately. In the later stage, only the improved NSGA-II was run. The proposed ECLUS guarantees the rapid convergence of ECL-INS-LMOA in the early evolution by accelerating particle update, introducing flight time, and proposing a binary competitive swarm optimizer BCSO. The results of the simulation imaging experiments on five large areas with different scales of decision variables show that ECL-INS-LMOA can always obtain the imaging satellite mission planning scheme with the highest regional coverage and the lowest satellite resource consumption within the limited evaluation times. The experiments verify the excellent performance of ECL-INS-LMOA in solving vast area mapping planning problems. satellite mission planning; vast area mapping; efficient competitive learning; large-scale optimization; multi-objective optimization +
Footnote †: journal: remote sensing  
0000-0002-4000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-0000-00000-0000-00  
imaging method of 'imaging wherever there is no image' for regional products, which lacks scientific and efficient planning methods. With the continuous increase in the number of satellites, the expansion of regional imaging product application fields, and the increasing demand for regional product timeliness, it is increasingly urgent to fully utilize existing satellite resources and efficiently obtain regional remote sensing images. Imaging satellite task planning is a technology that scientifically arranges imaging tasks for satellites based on user needs. It maximizes comprehensive imaging income by arranging each satellite to image each sub-region at the appropriate time and attitude based on user needs, satellite attribute information, and related constraints, such as energy constraints, maneuverability constraints, storage constraints, etc. [12; 13]. For regional mapping, the efficient utilization of satellite resources and the rapid completion of regional imaging tasks are the most important and commonly used imaging benefits. Mathematical modeling and optimization algorithm solving are two core steps in imaging satellite mission planning. Because the different needs of imaging satellite mission planning are often contradictory for mathematical modeling, it is often established as a model with multiple objective functions, known as multi-objective imaging satellite task planning. Correspondingly, multi-objective optimization algorithms (MOEAs) [14; 15] are used to solve multi-objective task planning models. Point targets and small area targets were commonly imaged in the past imaging satellite mission planning. General MOEAs, such as NSGA-II [16] and MOPSO [17], can find good solutions because the number of their imaging strips is small. That is, the scale of the planning problem is small.