## 2 Materials and Methods  
### A New Paradigm for Remote Sensing Image Interpretation  
In recent years, with the rapid development of large-scale satellite constellation technology, the resolution, update frequency, and spectral range of remote sensing data have rapidly increased. Combined with the characteristics of remote sensing data and the new capabilities brought by artificial intelligence, the interpretation technology of remote sensing images has been improved from manual interpretation to an intelligent one marked by rapid investigation and monitoring, scientific diagnosis and analysis, efficient decision-making and management. As shown in Figure 1, the current method of remote sensing intelligent interpretation is to use the HPC server on the ground segment to complete the intelligent interpretation of the remote sensing image after transmitting the remote sensing image data to the ground segment and generating remote sensing products for users.  
However, the post-processing and interpretation of remote sensing images on the ground still have certain limitations and room for optimization, such as poor timeliness and large link occupancy. Furthermore, HPC for interpreting remote sensing data will also consume power resources. The consumption of energy resources will increase significantly with the continuous increase in satellites and the generated remote sensing data.  
In order to optimize the above problems, this paper proposes a new remote sensing image processing system architecture, as shown in the right half of Figure 1, which transplants the remote sensing interpretation calculation from the ground segment to the space segment. On-board remote sensing interpretation in the space segment has the following advantage:  
1. More environmentally friendly: The satellite uses solar energy. Compared with computing on the ground which consumes the electronic resources on the earth, on-orbit computing uses clean energy provided by satellites to saves the electronic resources on the earth.
2. More timeliness: The remote sensing satellite are the front end of perception. On-orbit interpretation can complete the early discovery and continuous tracking of the target information of interest.
3. Easier access to IoT: Only transmitting meaningful semantic information instead of raw remote sensing data could reduce the bandwidth occupied by the inter-satellite link, making it easier to realize the collaborative work of multiple satellites.
4. Automatically improves the recognition performance of satellites in orbit: On-orbit semi-supervised training of intelligent models could be performed in real orbital work scenarios to improve performance.  
However, there are still the following challenges in performing on-orbit interpretation calculations:  
1. Compared with high-performance servers on the ground, the computing power and storage resources on satellites are more limited, so a more lightweight target detection algorithm is required, that is, a neural network model algorithm with lower computational energy consumption and less storage space.  
Figure 1: Comparison of traditional remote sensing interpretation methods and on-orbit interpretation methods.  
To achieve enough high precision requirements.  
Therefore, research on the algorithm of intelligent interpretation with lightweight, high precision, and low power consumption is the core problem. This research intends to focus on the new remote sensing image processing constitution of on-orbit interpretation, and face the practical problem of automatic ship target detection in SAR remote sensing images.  
According to the analysis in Section 1, the one-stage target detection framework has more lightweight characteristics. Based on a large number of investigations, we chose the SOTA algorithm YOLOv5 as the baseline technique to build a target detection framework suitable for the satellite platform.