Journal Article

Multi-Objective Multi-Satellite Imaging Mission Planning Algorithm for Regional Mapping Based on Deep Reinforcement Learning

Chen, Yaxin, Lu, Zezhong, Shen, Xin, Zhang, Guo

2023

Overview

Satellite imaging mission planning is used to optimize satellites to obtain target images efficiently. Many evolutionary algorithms (EAs) have been proposed for satellite mission planning. EAs typically require evolutionary parameters, such as the crossover and mutation rates. The performance of EAs is considerably affected by parameter setting. However, most parameter configuration methods of the current EAs are artificially set and lack the overall consideration of multiple parameters. Thus, parameter configuration becomes suboptimal and EAs cannot be effectively utilized. To obtain satisfactory optimization results, the EA comp ensates by extending the evolutionary generation or improving the evolutionary strategy, but it significantly increases the computational consumption. In this study, a multi-objective learning evolutionary algorithm (MOLEA) was proposed to solve the optimal configuration problem of multiple evolutionary parameters and used to solve effective imaging satellite task planning for region mapping. In the MOLEA, population state encoding provided comprehensive population information on the configuration of evolutionary parameters. The evolutionary parameters of each generation were configured autonomously through deep reinforcement learning (DRL), enabling each generation of parameters to gain the best evolutionary benefits for future evolution. Furthermore, the HV of the multi-objective evolutionary algorithm (MOEA) was used to guide reinforcement learning. The superiority of the proposed MOLEA was verified by comparing the optimization performance, stability, and running time of the MOLEA with existing multi-objective optimization algorithms by using four satellites to image two regions of Hubei and Congo (K). The experimental results showed that the optimization performance of the MOLEA was significantly improved, and better imaging satellite task planning solutions were obtained.

• Publisher: MDPI AG
• Subject: Algorithms / China / Configurations / Deep learning / deep reinforcement learning / Efficiency / evolution / Evolutionary algorithms / Genetic algorithms / Geospatial data / Image processing / Learning / Machine learning / Mapping / Mathematical optimization / Methods / Mission planning / multi-objective optimization / Multiple objective analysis / Mutation / Mutation rates / Optimization / Parameters / regional mapping / Regional planning / Reinforcement / Reinforcement learning (Machine learning) / Remote sensing / Republic of the Congo / Satellite imagery / Satellite imaging / satellite imaging mission planning / Satellites