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A novel approach for space debris recognition based on the full information vectors of star points

机译：基于星点的完整信息向量的空间碎片识别的新方法

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摘要

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. (c) 2019 Elsevier Inc. All rights reserved.
机译：空间碎片的识别和检测已成为最近的重要研究领域之一。与自然图像相比，恒星图像中包含有效信息。在过去几年中，通过许多算法利用了星点的灰度和连续星图像的连续性，以通过融合通过连续的星形图像进行识别和检测，这已经实现了良好的性能。然而，随着星形图像数据的快速增加，这些算法似乎是识别能力不充分。在本文中，我们提出了一种基于星形点的完整信息矢量的一种新颖方法，以识别与机器学习方法的移动目标，从未在空间碎片识别场中使用。除了灰度值之外，我们还通过高斯分布的等概率密度曲线进一步深深地挖掘了一个帧中的每个星点的特征。星点的椭圆图案特征向量可以输入机器学习方法，用于分类静态恒星和单个帧中的移动目标。最后，可以通过完整信息向量在3帧内确定移动目标的轨迹。因此，抛弃了传统的加工方法，拟议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，移动目标可以在单个帧中成功识别，并且移动目标的覆盖率可以达到100％。与其他传统方法相比，建议的方法具有更好的性能和更具稳健性。 （c）2019 Elsevier Inc.保留所有权利。

著录项

  • 来源
    《Journal of visual communication & image representation》 |2020年第8期|102716.1-102716.13|共13页
  • 作者

    Du Yun; Wen Desheng; Liu Guizhong; Qiu Shi; Yao Dalei; Yi Hongwei; Liu Meiying;

  • 作者单位

    Chinese Acad Sci Xian Inst Opt & Precis Mech Xian 710119 Peoples R China|Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China|Univ Chinese Acad Sci Beijing 100049 Peoples R China;

    Chinese Acad Sci Xian Inst Opt & Precis Mech Xian 710119 Peoples R China;

    Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China;

    Chinese Acad Sci Xian Inst Opt & Precis Mech Xian 710119 Peoples R China;

    Chinese Acad Sci Xian Inst Opt & Precis Mech Xian 710119 Peoples R China|Xi An Jiao Tong Univ Sch Elect & Informat Engn Xian 710049 Peoples R China|Univ Chinese Acad Sci Beijing 100049 Peoples R China;

    Chinese Acad Sci Xian Inst Opt & Precis Mech Xian 710119 Peoples R China;

    Chinese Acad Sci Xian Inst Opt & Precis Mech Xian 710119 Peoples R China|Univ Chinese Acad Sci Beijing 100049 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
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  • 关键词

    Space debris recognition; Star image; Binary classifier; Equal probability density curve; Full information vector;

    机译：空间碎片识别;星图像;二进制分类器;等概率密度曲线;完整信息矢量;

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