A Multi-Objective Modeling Method of Multi-Satellite Imaging Task Planning for Large Regional Mapping

Abstract

Regional remote sensing image products are playing an important role in an increasing number of application fields. Aiming at multi-satellite imaging task planning for large-area image acquisition, this paper proposes a multi-objective modeling method. First, we analyzed the core requirements of regional mapping for multi-satellite imaging mission planning: Full coverage of the target area and low consumption of satellite resources. Second, an optimization model with two objective functions, namely the maximum target area coverage and minimum satellite resource utilization, was established. Using the selection of imaging strips and their swing angles as two types of decision variables, the regional decomposition and satellite resource allocation were integrated into the planning model. Third, two efficient algorithms, Vatti and non-dominated sorting genetic algorithm (NSGA-II), were used for objective function calculation and model solving, respectively. Finally, the experiments used Hubei, Finland, and Congo as the target areas and GF1, GF6, ZY1-02C, and ZY3 as imaging satellites to verify the modeling method proposed in this paper. The experiments showed that the proposed multi-objective modeling method could complete the coverage of regional targets with fewer satellite resources and improve the satellite application efficiency significantly.

Large region targets observation scheduling by multiple satellites using resampling particle swarm optimization

“…Single EOS Exact [6], [7], Heuristic [8], [9] − Multiple EOSs MOEA [10], [11], Heuristic [12], [13], [14], [15] MOEA [16], [17], Heuristic [18], [19], [20], [21], [22], [23] Large A considerable amount of literature has been published on the point targets observation scheduling problem over the past decades. Existing algorithms to solve the Earth observation scheduling problem can be divided into three types: the exact algorithm [24], [25], [26], heuristic [27], [28], [29], [30], and multi-objective evolutionary algorithm (MOEA) [31].…”

“…As for the multi-region observation, Li et al [16] introduced the preference incorporation to the region targets observation scheduling problem and established a multi-objective optimization model considering the profit, quality, and timeliness simultaneously. In order to maximize the target area coverage and minimize the observation resource utilization, Chen et al [17] adopted a non-dominated sorting genetic algorithm to solve the model. Wang et al [18] established a nonlinear model and presented a heuristic for solving the disaster monitoring observation scheduling problem by four satellites.…”

“…Although the above research has been carried out on area target observation by EOSs, few studies pay sufficient attention to the large region target except for [11], [17], [22], [23]. A possible explanation might be that there exist extensive candidate tasks derived from a large region target, which makes the coverage calculation and scheduling process more difficult.…”

“…As a core of the proposed improved RPSO algorithm, the resampling strategy contributes to generating a better scheduling solution within given iteration steps. In the standard PSO algorithm, the particle velocity will be updated as Equation ( 16) and then the new particle position can be generated according to Equation (17). Generally, the gene value of each newly generated particle position may be out of the valid range…”

A Cooperative Autonomous Scheduling Approach for Multiple Earth Observation Satellites With Intensive Missions

“…Refs. [16], [17] used non-dominated sorting genetic algorithm-II (NSGA-II) to solve the multi-satellite mission planning problem for large-area image acquisition. Sarkheyli et al [18] presented a new tabu search algorithm for resource scheduling of low earth orbit (LEO) satellites mission.…”