Zhu Rui, Fu Qiang, Liu Nan, Zhao Feng, Wen Guanyu, Li Yingchao, Jiang Huilin
Jilin Provincial Key Laboratory of Space Optoelectronics Technology, Changchun University of Science and Technology, Changchun, China.
Institute of Optoelectronic Engineering, Changchun University of Science and Technology, Changchun, China.
Sci Rep. 2024 Jan 21;14(1):1832. doi: 10.1038/s41598-024-51717-0.
The detection of faint and small targets by space-based surveillance systems is difficult owing to the long distances, low energies, high speeds, high false alarm rates, and low algorithmic efficiencies involved in the process. To improve space object detection and help prevent collisions with critical facilities such as satellites, this study proposes an improved method for the detection of faint and small space-based targets. The proposed method consists of two components: star atlas preprocessing and space-based target detection. The star atlas preprocessing step applies multi-exposure image pyramidal weighted fusion to the original image containing the faint and small space-based target. After obtaining the image pyramidal weighted fusion result atlas, the algorithm employs threshold segmentation to improve the overall image clarity, highlight image details, and provide additional information for target detection. The detection of targets partially relies on the local symmetry of the image. Accordingly, a diffusion function describing the local symmetry is established to precisely locate stars by measuring the symmetry factor in a small area surrounding each pixel in the star atlas. This effectively removes the background stars while retaining high-definition and high-contrast images. The efficacy of the algorithm is validated using simulated datasets consisting of space-based and real images. The results demonstrate that the proposed technique improves the applicability of the multistage hypothesis testing (MHT) method in the context of a complex space environment, thus improving the performance of the space-based electro-optical detection system to better catalogue, identify, and track space targets.
由于天基监视系统在探测微弱和小目标时涉及距离远、能量低、速度高、误报率高以及算法效率低等问题，所以探测困难。为了改进空间目标探测并帮助防止与卫星等关键设施发生碰撞，本研究提出了一种改进的微弱和小天基目标探测方法。所提出的方法由两部分组成：星图预处理和天基目标探测。星图预处理步骤对包含微弱和小天基目标的原始图像应用多曝光图像金字塔加权融合。在获得图像金字塔加权融合结果图后，该算法采用阈值分割来提高整体图像清晰度，突出图像细节，并为目标探测提供额外信息。目标探测部分依赖于图像的局部对称性。因此，建立了一个描述局部对称性的扩散函数，通过测量星图中每个像素周围小区域的对称因子来精确地定位恒星。这有效地去除了背景恒星，同时保留了高清和高对比度的图像。使用由天基和真实图像组成的模拟数据集验证了该算法的有效性。结果表明，所提出的技术提高了多阶段假设检验（MHT）方法在复杂空间环境中的适用性，从而提高了天基光电探测系统对空间目标进行更好编目、识别和跟踪的性能。
