Detection of small space target based on iterative distance classification and trajectory association

YAO Rui; ZHANG Yan-ning; YANG Tao; DUAN Feng

doi:10.3788/OPE.20122001.0179

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Detection of small space target based on iterative distance classification and trajectory association

Article | 更新时间：2020-08-12

- Detection of small space target based on iterative distance classification and trajectory association
- Optics and Precision Engineering Vol. 20, Issue 1, Pages: 179-189(2012)
- 作者机构：
  
  西北工业大学计算机学院陕西省语音与图像信息处理重点实验室,陕西西安,710129
- 作者简介：
- 基金信息：
- DOI：10.3788/OPE.20122001.0179
  CLC： TP391
- Received：23 June 2011，
  
  Revised：17 August 2011，
  
  Published Online：25 January 2012，
  
  Published：25 January 2012
- 稿件说明：
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姚睿, 张艳宁, 杨涛, 段锋. 基于迭代距离分类与轨迹关联检测空间弱小目标[J]. 光学精密工程, 2012,20(1): 179-189

YAO Rui, ZHANG Yan-ning, YANG Tao, DUAN Feng. Detection of small space target based on iterative distance classification and trajectory association[J]. Editorial Office of Optics and Precision Engineering, 2012,20(1): 179-189
姚睿, 张艳宁, 杨涛, 段锋. 基于迭代距离分类与轨迹关联检测空间弱小目标[J]. 光学精密工程, 2012,20(1): 179-189 DOI： 10.3788/OPE.20122001.0179.

YAO Rui, ZHANG Yan-ning, YANG Tao, DUAN Feng. Detection of small space target based on iterative distance classification and trajectory association[J]. Editorial Office of Optics and Precision Engineering, 2012,20(1): 179-189 DOI： 10.3788/OPE.20122001.0179.

摘要

为了实现高效自动目标检测

提出了一种可用于低信噪比条件下的空间可见光弱小目标检测算法。首先

建立空间光学图像模型

利用恒虚警率(CFAR)方法确定分割系数对单帧图像背景进行分割;然后

基于恒星结构稳定特性构建距离特征空间

并针对特征空间构造分类准则函数

使用迭代最优化分类方法提取出候选目标点;最后

依据目标运动轨迹的连续性建立空间目标轨迹关联、合并以及虚假目标轨迹删除规则

进行轨迹处理

实现空间可见光弱小目标的检测。文中还提出了单帧检测率、虚警率与序列检测率、虚警率相结合的评价方法。实验结果表明:在低信噪比条件下(SNR3)

得到的序列检测率达到96.02%以上

序列虚警率达到4.4%以下。该方法在低信噪比条件下显著提高了目标检测率

并有效抑制了虚警。

Abstract

To realize automatic target detection

an algorithm is proposed to detect small visible optical space targets against low SNR conditions. Firstly

the single-frame image background is segmented

and the segmentation coefficient is determined by a Constant False Alarm Ratio (CFAR) method. Then

a feature space is formed based on structural stability of the star

and classification criterion function is constructed for the distance feature space. Furthermore

candidate targets are extracted by using the iterative optimization distance classification method. Finally

small visible optical space targets are detected by trajectory association based on the continuity of target motion. In addition

an evaluation method combined with single frame detection probability

single frame false alarm probability and sequence detection probability is proposed. Experimental results indicate that the detection probability of sequence is more than 96.02%

and the false alarm probability is less than 4.4% when the SNR3. It concludes that the method can promote the detection probability against low SNR conditions significantly

and can remove the false alarm effectively.

关键词

Keywords

references

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