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Image transfer plays a significant role in the manufacture of PCB; it affects the production speed and quality of the manufacturing process. This study proposes a surface-framework structure, which divides the network into two parts: surface and framework. The surface part does not include subsampling to extract the detailed features of the image, thereby improving the segmentation effect when the computing power requirement is not large. Meanwhile, a semantic segmentation method based on Unet and surface-framework structure, called pure efficient Unet (PE Unet), is proposed. A comparative experiment is conducted on our mark-point dataset (MPRS). The proposed model achieved good results in various metrics. The proposed network’s IoU attained 84.74%, which is 3.15% higher than Unet. The GFLOPs is 34.0 which shows that the network model balances performance and speed. Furthermore, comparative experiments on MPRS, CHASE_DB1, TCGA-LGG datasets for Surface-Framework structure are introduced, the IoU promotion clipped means on these datasets are 2.38%, 4.35% and 0.78% respectively. The Surface-Framework structure can weaken the gridding effect and improve the performance of semantic segmentation network.

Aiming at the problem of how to improve the requirement satisfaction of multi-point target imaging reconnaissance tasks and the utilization efficiency of satellite resources, a multi-point target imaging reconnaissance requirement fusion method based on genetic optimization is proposed in this paper. Firstly, the basic constraints that can be fused between multi-point targets are analysed, and on this basis, a point-target synthetic observation model is constructed. Secondly, considering the constraints of observation time window, observation angle and transition time between synthetic tasks, a genetic algorithm-based multi-point target requirements fusion method is designed. Finally, through the simulation analysis, it is concluded that with the continuous increase of user requirements, the fusion method based on genetic optimization can still achieve better task satisfaction to the user’s imaging requirements, which greatly improves the use efficiency of imaging satellites.

Aiming at how to solve the problem of optimal matching between imaging reconnaissance user requirements and satellite resources, this paper conducts research on the preprocessing of imaging reconnaissance satellite requirements. First, considering the remote sensor type and resolution of imaging satellites, a requirement-resource matching matrix is constructed to realize the preliminary matching between user requirements and imaging reconnaissance satellite resources. Secondly, starting from the time constraints, by calculating the observation time window of requirement and resources, the time requirements of satellite resources and user requirements are matched. Finally, in order to solve the problem that the same observation task can be completed by multiple imaging satellites, a conflict resolution method based on resource consumption is proposed, which effectively improves the preprocessing ability of user requirements.