# Intelligent Space Object Detection Driven by Data from Space Objects
## 2 Dataset Construction and Labeling  
### Dataset Construction  
It is challenging to collect such data due to the limitations of observation conditions and external environments. However, the load structure and characteristics of various space objects are known to the observer. Through computer simulation, it is possible to generate a large number of synthetic images with labeled information under some particular observational conditions. When these synthetic images are combined with a small proportion of real data, transfer learning is performed to detect space objects [27]. In the present study, 49 3D satellite models are applied to create a dataset with a comprehensive orientation. The initial dataset consists of over 9600 images, including 2300 images from the BUAA-SID 1.0 dataset. It was constructed by Beihang University in response to the needs of a specific project, whose contents have not been fully disclosed. The meaning of BUAA-SID is not discernable from the available information. In the original paper, it is referred to as the Satellite Image Dataset (640 \(\times\) 480 resolution). It includes 20 satellite models [28], 6200 images (640 \(\times\) 480 resolution) with 29 satellite models, and 500 actual satellite images. An additional 500 images were collected online. Due to the discrepancy in image size between the satellite dataset and the dataset collected online, they were uniformly resized to 600 \(\times\) 600. Finally, a large-scale satellite dataset with two different sizes was created. Based on space object 3D models, the BUAA-SID 1.0 space object image database uses 3 ds Max 2009 software to produce the sequence of a full-viewpoint simulation image. It involves 56 3D satellite models and their simulation images. The BUAA-SID 1.0 simulated image database consists of 25,760 simulated images whose resolution is 320 \(\times\) 240 pixels, with 460 images in each satellite. Among them, 230 images are full-view 24-bit color images, and the other 230 images are the corresponding binary images. With 20 full-view 24-bit color satellite simulation images selected from the BUAA-SID 1.0 dataset, 115 images of various postures are chosen for each satellite. Through bilinear interpolation, the resolution of these images increased to 640 \(\times\) 480 pixels. Figure 1 shows some of the satellite images. In this paper, the process of generating a space object image by simulation is detailed as follows:  
Due to the limited amount of data available in the BUSA-SID1.0 dataset, it is difficult to perform deep learning on large datasets. Therefore, the construction of the BUAA-SID1.0 dataset is simulated and the software Blender3.4 is applied to model 29 different types of space objects. In order to reflect the dynamic characteristics of these space objects faithfully, their motion is simulated by rotating and moving these models in the Blender software. In the 3D model library, the strong rendering capability of Blender is exercised to conduct imaging simulation of 3D models, as shown in Figure 2.  
Figure 1: Partial satellite images obtained from the BUAA-SID1.0. The critical steps in the generation of images within the simulation image library are detailed as follows:  
1. Camera Setup: In space-based visible light imaging detection of space targets, there is a long distance between the space target and the camera, and the distance of observation far exceeds the size of the space observation target. Therefore, orthographic projection is performed in the process of simulation. Also, a camera is used for alignment with the satellite, with a field of view set to 45\({}^{\circ}\). Thus, both are aligned with the z-axis. This allows the model to be positioned at the center of the camera in the initial state, which makes all parts visible. 2. Light Source Setup: In the space environment, sunlight is almost comparable to parallel light. Hence, a parallel white light source is introduced at the initial position of the camera. Without attenuation, it has an intensity multiplier coefficient of 0.906.