The recognition and detection of space debris has become one of significant research fields recently. Compared with natural images, effective information are very few contained in star images. In the past years, the gray values of star points and the continuity of sequential star images are utilized by numerous algorithms to carry out the recognition and detection through fusion of consecutive star images, which have been achieved good performance. However, with the rapid increase of star image data, those algorithms seem to be inadequate in recognition ability. In this paper, we propose one novel approach based on the full information vectors of star points to recognize moving targets with the machine learning method which is never utilized in space debris recognition field. Besides gray values, we further deeply excavate the characteristics of each star point in a single frame by the equal probability density curve of Gaussian distribution. The elliptical pattern characteristic vectors of star points can be input into the machine learning method for classification of static stars and moving targets in a single frame. Finally, trajectories of moving targets can be determined within 3 frames by the full information vectors. Therefore, traditional processing methods are abandoned and the proposed brand new approach redefines the recognition technical route of space debris. The experimental results demonstrate that moving targets can be successfully recognized in a single frame and the coverage rate of moving targets can reach 100%. Compared with other traditional methods, the proposed approach has better performance and more robustness.

空间碎片的识别和检测已成为近来的重要研究领域之一。与自然图像相比，恒星图像中很少包含有效信息。近年来，许多算法利用星点的灰度值和连续星图的连续性，通过融合连续星图进行识别和检测，取得了良好的性能。但是，随着恒星图像数据的迅速增加，这些算法的识别能力似乎不足。在本文中，我们提出了一种基于星点的全部信息矢量的新颖方法，该方法利用机器学习方法来识别运动目标，这在空间碎片识别领域从未使用过。除了灰度值，我们通过高斯分布的等概率密度曲线进一步深入挖掘单个帧中每个星点的特征。可以将星点的椭圆形特征向量输入到机器学习方法中，以在单个帧中对静态星和运动目标进行分类。最后，可以通过完整的信息矢量在3帧内确定运动目标的轨迹。因此，传统的处理方法被抛弃，提出的全新方法重新定义了空间碎片的识别技术路线。实验结果表明，可以在单个帧内成功识别运动目标，运动目标的覆盖率可以达到100％。与其他传统方法相比，该方法具有更好的性能和更强的鲁棒性。