Infrared Small-target Detection Using The Maximum of Second-order Directional Derivative

ZHAO Ai-gang; WANG Hong-li; YANG Xiao-gang; LU Jing-hui; JIANG Wei; QI Nai-xin

doi:10.3788/fgxb20163709.1142

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Infrared Small-target Detection Using The Maximum of Second-order Directional Derivative

更新时间：2020-08-12

- Infrared Small-target Detection Using The Maximum of Second-order Directional Derivative
- Chinese Journal of Luminescence Vol. 37, Issue 9, Pages: 1142-1151(2016)
- 作者机构：
  
  1. 火箭军工程大学控制工程系,陕西西安,710025
  2. 火箭军工程大学士官学院, 山东青州,262500
- 作者简介：
- 基金信息：
- DOI：10.3788/fgxb20163709.1142
  CLC： TP394.1
- Received：10 April 2016，
  
  Revised：10 May 2016，
  
  Published：05 September 2016
- 稿件说明：
移动端阅览
赵爱罡, 王宏力, 杨小冈等. 利用二阶方向导数极大值检测红外小目标[J]. 发光学报, 2016,37(9): 1142-1151

ZHAO Ai-gang, WANG Hong-li, YANG Xiao-gang etc. Infrared Small-target Detection Using The Maximum of Second-order Directional Derivative[J]. Chinese Journal of Luminescence, 2016,37(9): 1142-1151
赵爱罡, 王宏力, 杨小冈等. 利用二阶方向导数极大值检测红外小目标[J]. 发光学报, 2016,37(9): 1142-1151 DOI： 10.3788/fgxb20163709.1142.

ZHAO Ai-gang, WANG Hong-li, YANG Xiao-gang etc. Infrared Small-target Detection Using The Maximum of Second-order Directional Derivative[J]. Chinese Journal of Luminescence, 2016,37(9): 1142-1151 DOI： 10.3788/fgxb20163709.1142.

摘要

为提高复杂环境下红外小目标的检测率，提出了基于二阶方向导数极大值的红外小目标检测算法。该算法首先对二阶方向导数的性质进行了分析，对极大值进行阈值翻转操作，将背景中的平坦成分和边缘成分剔除。接着，根据小面模型对背景进行预测，并以预测误差为权值进一步增强小目标区域。以上2个步骤的计算可通过4个卷积实现，加快了检测速度。最后，对少量候选小目标计算局部对比度，降低了虚警率。实验结果表明：该检测算法在6种复杂背景下平均信杂比增益为78.413 0，平均背景抑制因子为35.079 6，具有较强的鲁棒性和较高的检测率。

Abstract

In order to improve the detection rate of infrared small-target in complex environment

a infrared small-target detection algorithm based on the maximum of second-order directional derivative was proposed. Firstly

the properties of second-order directional derivative were analyzed

meanwhile

the flat component and edge of background were removed by threshold and flip operations of the maximum. Then

the background was predicted based on facet model and further enhanced the small-target by prediction error as weight. The above two steps can be achieved by four convolutions and the detection speed was accelerated. At last

the local contrast of candidate small-targets was calculated to reduce the false alarm rate. The experimental results show that the average signal to clutter ratio gain is 78.413 0 and the average background suppression factor is 35.079 6 in 6 kinds of complex background. The proposed detection algorithm has stronger robustness and higher detection rate.

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references

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BAI X Z, ZHOU F G. Analysis of new top-hat transformation and the application for infrared dim small target detection[J]. Pattern Recognit., 2010, 43(6):2145-2156.

WANG G D, CHEN C Y, SHEN X B. Facet-based infrared small target detection method[J]. Electron. Lett., 2005, 41(22):1244-1246.

WANG P, TIAN J W, GAO C Q. Infrared small target detection using directional highpass filters based on LS-SVM[J]. Electron. Lett., 2009, 45(3):156-158.

HAN J H, MA Y, ZHOU B, et al.. A robust infrared small target detection algorithm based on human visual system[J]. IEEE Geosci. Remote Sens. Lett., 2014, 11(12):2168-2172.

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