Recently, remote sensing satellites have become increasingly important in the Earth observation field as their temporal, spatial, and spectral resolutions have improved. Subsequently, the quantitative evaluation of remote sensing satellites has received considerable attention. The quantitative evaluation method is conventionally based on simulation, but it has a speed-accuracy trade-off. In this paper, a real-time evaluation model architecture for remote sensing satellite clusters is proposed. Firstly, a multi-physical field coupling simulation model of the satellite cluster to observe moving targets is established. Aside from considering the repercussions of on-board resource constraints, it also considers the consequences of the imaging’s uncertainty effects on observation results. Secondly, a moving target observation indicator system is developed, which reflects the satellite cluster’s actual effectiveness in orbit. Meanwhile, an indicator screening method using correlation analysis is proposed to improve the independence of the indicator system. Thirdly, a neural network is designed and trained for stakeholders to realize a rapid evaluation. Different network structures and parameters are comprehensively studied to determine the optimized neural network model. Finally, based on the experiments carried out, the proposed neural network evaluation model can generate real-time, high-quality evaluation results. Hence, the validity of our proposed approach is substantiated.

The number of remote sensing satellites has increased rapidly in parallel with the advancement of space technology and the rising demand in the space industry. Consequently, the observation effectiveness evaluation of remote sensing satellites has received extensive attention. As the core content of the effectiveness evaluation, index systems are usually established and screened using qualitative or quantitative methods. They can hardly satisfy the construction principles such as completeness and independence simultaneously. To address this issue, we propose a new method for remote sensing satellite observation effectiveness evaluation that considers various principles. Firstly, a three-layer evaluation index system structure is constructed. The principle of completeness, hierarchy, and measurability of the index system is ensured by decomposition, clustering, and preliminary screening. Secondly, the principal component contribution rate is obtained through principal component analysis. Finally, we introduce a comprehensive scoring method (ICCLR) based on the combination of independence coefficient and principal component comprehensive loss rate. It realizes the screening of an index system from the index set containing correlation relationships. The validity and optimality of the proposed method are verified through experiments and analysis of three typical tasks.

The remote sensing satellite cluster system, as an important component of the next generation of space architecture in the United States, has important application prospects in the military field. In order to improve the effects of time, with regard to the effectiveness evaluation of the remote sensing satellite cluster system, neural network methods are generally used to satisfy the requirements of real-time decision-making assistance in the military field. However, there are two problems that emerge when applying the existing neural network methods to an effectiveness evaluation of the remote sensing satellite cluster. On the one hand, the neural network model architecture needs to be designed specifically for the remote sensing satellite cluster system. On the other hand, there is still a lack of hyperparameter optimization methods that consume less time and have good optimization effects for the established neural network model. In this regard, two main modifications were made to the back-propagation neural network, to which an effectiveness evaluation was applied. The first comprised a new architecture named BPS, which was designed for the back-propagation neural network so as to improve its prediction accuracy. In BP architecture, one back-propagation neural network is established for each indicator involved in the effectiveness evaluation indicator system of the remote sensing satellite cluster; the output of each back-propagation neural network model is modified to the residual value between the corresponding indicator value and the value that is predicted through a multiple linear regression analysis of the corresponding indicator. The second modification involved the multi-round traversal method, which is based on the three-way decision theory, and it was proposed in order to significantly improve the model’s training time, which is a new type of hyperparameter optimization method. The results show that compared with the traditional simulation model, the modified back-propagation neural network model based on three-way decision theory can quickly and effectively provide stable and accurate evaluation results; this can assist with and meet the requirements for real-time decision-making in the military field.