基于改进MTI算法的视频图像空间目标检测

罗振杰, 曾国强. 基于改进MTI算法的视频图像空间目标检测[J]. 光电工程, 2018, 45(8): 180048. doi: 10.12086/oee.2018.180048
引用本文: 罗振杰, 曾国强. 基于改进MTI算法的视频图像空间目标检测[J]. 光电工程, 2018, 45(8): 180048. doi: 10.12086/oee.2018.180048
Luo Zhenjie, Zeng Guoqiang. Space objects detection in video satellite images using improved MTI algorithm[J]. Opto-Electronic Engineering, 2018, 45(8): 180048. doi: 10.12086/oee.2018.180048
Citation: Luo Zhenjie, Zeng Guoqiang. Space objects detection in video satellite images using improved MTI algorithm[J]. Opto-Electronic Engineering, 2018, 45(8): 180048. doi: 10.12086/oee.2018.180048

基于改进MTI算法的视频图像空间目标检测

详细信息
    作者简介:
    通讯作者: 曾国强(1973-),男,博士,教授,主要从事微小卫星总体技术的研究。E-mail:Gq.zeng@whu.edu.cn
  • 中图分类号: TP391

Space objects detection in video satellite images using improved MTI algorithm

More Information
  • 针对经典MTI算法在处理某型微纳卫星拍摄的视频图像时存在较为耗时的现象,以及空间目标轨迹投影不连续造成无法使用连通域标记同一目标的问题,本文提出一种改进的MTI算法用于空间目标检测。算法通过设计像素“感受域”,消除了空间目标轨迹投影不连续的现象。同时,在简化了像素时序信号投影的步骤后,仍能保留原算法对背景杂光和噪声的滤除作用,并使得算法速度得到提升。基于某型微纳卫星拍摄的视频图像进行算法实验,结果表明,本文算法对于轨迹投影不连续空间目标的检测无虚警,算法速度约为0.06 s/f。

  • Overview: Space exploration activities are becoming more frequent, resulting in an increased population of space debris and even an increased risk of on-orbit collisions. In order to prevent on-orbit collision accidents, many countries have conducted space objects detection projects. Space-based observation is an important and effective means for the perception of space objects, attributing to its advantages of being closer to space objects and not constrained by weather and location. With the development of microsatellite technology in recent years, various countries have carried out space-based observations based on microsatellites, such as the STARE Project of US, the three microsatellites of MOST, Sapphire and NEOSSat in Canada. In order to successfully locate the space objects, the realization of dim targets detection based on the sky background image is one of the key technologies. The American SBV plan proposed a classic space objects detection process, which consists of a classical MTI algorithm and a speed filter. However, this algorithm has a long running time. Some scholars also use MTI algorithm and connected domain detection to detect space objects, but they do not consider the situation where the trajectory is discontinuous when the object's speed is a little bit large. To solve the above problems, an improved MTI algorithm is proposed in this paper. We aim to realize the space objects detection in a video satellite's images. In order to detect the inconsecutive target's trajectory, at the beginning of the algorithm we set a special preprocessing which is called pixel's feeling domain. To reduce the time of the algorithm, we simplified the time projection part of the classic MTI algorithm, which is used to restrain the background. After the improved MTI algorithm is processed, targets' trajectories are then obtained through feature-based connected domain detection. When we get the target's trajectory, we can locate the target's position in the image by the centroid method. In summary, the contribution of this paper, namely the improvement of the MTI algorithm, mainly has two points: 1) introduced pixel's feeling domain preprocessing, 2) simplified the time projection part. The experiments in a video satellite's images show that, the improved MTI algorithm can effectively eliminate the background and is suitable for the inconsecutive target's trajectory detection. In addition, the algorithm's processing speed almost meets the real-time task.

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  • 图 1  SBV经典检测算法流程框架

    Figure 1.  The classical detection algorithm framework of SBV

    图 2  本文算法流程框架

    Figure 2.  The algorithm framework in this paper

    图 3  相邻两帧的目标位置

    Figure 3.  Target's position in adjacent frames

    图 4  相邻两帧的目标位置和“感受域”示意图

    Figure 4.  Target's position in adjacent frames and the middle pixel's feeling domain

    图 5  某一帧视频图像截图。(a)视频1;(b)视频2

    Figure 5.  A frame of video image capture. (a) Video 1; (b) Video 2

    图 6  本文算法对两段视频的检测结果图。(a)视频1;(b)视频2

    Figure 6.  The detection results of the algorithm in this paper. (a) The detection result of video 1; (b) The detection result of video 2

    图 7  有无设置像素“感受域”的对比。(a)视频1设置像素“感受域”的目标轨迹检测效果局部放大图;(b)视频2设置像素“感受域”的目标轨迹检测效果局部放大图;(c)视频1无设置像素“感受域”的目标轨迹检测效果局部放大图;(d)视频2无设置像素“感受域”的目标检测效果局部放大图

    Figure 7.  Contrast before and after setting pixel's feeling domain. (a) Partial map of the object trajectory while using pixel's feeling domain setting in video 1; (b) Partial map of the object trajectory while using pixel's feeling domain setting in video 2; (c) Partial map of the object trajectory while no pixel's feeling domain setting in video 1; (d) Partial map of the object trajectory while using pixel's feeling domain setting in video 2

    表 1  算法耗时对比

    Table 1.  Time consuming contrast

    帧数 文献[9]对经典MTI的修改方法 本文算法
    视频1/s 视频2/s 视频1/s 视频2/s
    10 10.67 10.28 0.80 0.73
    30 13.51 12.68 1.88 1.85
    50 15.74 14.12 3.03 3.00
    下载: 导出CSV

    表 2  定位结果评估

    Table 2.  Evaluation of positioning accuracy

    视频序号 人工标注 本文算法
    起始帧位置 结束帧位置 起始帧位置 结束帧位置
    1 x:455, y:68 x:474, y:96 x:454.13, y:67.74 x:474.03, y:96.04
    2 x:51, y:98 x:73, y:155 x:49.90, y:98.07 x:73.68, y:154.98
    下载: 导出CSV
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出版历程
收稿日期:  2018-01-26
修回日期:  2018-04-20
刊出日期:  2018-08-01

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