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.

Multi-Satellite Imaging Task Planning for Large Regional Coverage: A Heuristic Algorithm Based on Triple Grids Method

“…Exact algorithm [23,24], branch and price algorithm [25], three-phase method [26], column generation method and implicit enumeration algorithm [27] Heuristic algorithm [21,22] tabu search [28], heuristic [29], NSGA-II [30,31], NSGA-III [32], three-phase solving framework [33,34], Two-Archive2 algorithm [35], PSO [36], GA [37] ECL-INS-LMOA [38], Reinforcement Learning [39] The multi-satellite scheduling problem for regional mapping is a highly complex combinatorial optimization problem. Exact algorithms are only applicable to small-scale examples due to the computational complexity involved.…”

Dynamic Task Planning Method for Multi-Source Remote Sensing Satellite Cooperative Observation in Complex Scenarios

“…In terms of model solving approaches, a suite of algorithms has been established, encompassing deterministic algorithms such as branch and bound and integer programming, as well as heuristic algorithms such as tabu search, simulated annealing, and mosquito swarm algorithms [7][8][9][10][11]. These methods are effective at addressing both single satellite mission planning and multi-satellite collaborative task planning issues [12][13][14][15]. Current research on single and multi-satellite observation mission planning has culminated in a relatively mature set of methodologies.…”