采用视觉特征整合的红外弱小目标检测

赵尚男; 王灵杰; 张新; 吴洪波

doi:10.3788/OPE.20202802.0497

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采用视觉特征整合的红外弱小目标检测

信息科学 | 更新时间：2020-07-16

- 采用视觉特征整合的红外弱小目标检测
- Detection of infrared dim small target based on visual feature integration
- 光学精密工程 2020年28卷第2期页码：497-506
- 作者机构：
  
  1.中国科学院长春光学精密机械与物理研究所中国科学院光学系统先进制造技术重点实验室，吉林长春 130033
  2.中国科学院大学，北京 100049
- 作者简介：
  
  [ "赵尚男 (1993—)，女，吉林长春人，硕士，研究实习员，分别于2015年、2018年于北京理工大学获得学士和硕士学位，主要从事计算成像、机器视觉、光学设计的研究。E-mail:18810575846@163.com" ]
  [ "王灵杰 (1979—)，男，江苏常州人，博士研究生，副研究员，1998年于天津大学获得学士学位，2002年于中科院长春光学精密机械与物理研究所获得硕士学位，主要从事红外光学系统设计及计算成像等方面的研究。E-mail: wanglingjie@126.com" ]
- 基金信息：
  
  国家自然科学基金资助项目(61675100);国家重点研发计划资助项目(2016YFB0501002);高分辨率对地观测重大专项资助项目(30-Y20A02-9003-17/18)
- DOI：10.3788/OPE.20202802.0497
  中图分类号： TP394.1;TN911.73;TP391.4
- 收稿日期：2019-07-29，
  
  修回日期：2019-10-16，
  
  录用日期：2019-10-16，
  
  纸质出版日期：2020-02-25
- 稿件说明：
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赵尚男, 王灵杰, 张新, 等. 采用视觉特征整合的红外弱小目标检测[J]. 光学精密工程, 2020,28(2):497-506.

Shang-nan ZHAO, Ling-jie WANG, Xin ZHANG, et al. Detection of infrared dim small target based on visual feature integration[J]. Optics and precision engineering, 2020, 28(2): 497-506.
赵尚男, 王灵杰, 张新, 等. 采用视觉特征整合的红外弱小目标检测[J]. 光学精密工程, 2020,28(2):497-506. DOI： 10.3788/OPE.20202802.0497.

Shang-nan ZHAO, Ling-jie WANG, Xin ZHANG, et al. Detection of infrared dim small target based on visual feature integration[J]. Optics and precision engineering, 2020, 28(2): 497-506. DOI： 10.3788/OPE.20202802.0497.

摘要

针对红外光学系统在复杂背景下的弱小目标检测问题，建立了基于特征整合的信息处理模型，提出了采用视觉特征整合的弱小目标检测方法。该方法首先利用视网膜神经节细胞感受野的数学模型DOG(Different-of-Gaussian)对红外图像进行初级信息处理，初步检测出弱小目标。而后，分为空域和频域两个通道进行特征提取。在空域通道，利用图像信息构造二阶微分Hessian矩阵，通过计算其直迹与行列式进行局部极值的判定，提取出含有弱小目标的结构分量特征；在频域通道，利用小波对图像频域进行二级分解，提取出含有弱小目标的高频分量特征。最后，将空域通道与频域通道的分量特征整合，提取出复杂背景下的弱小目标。实验结果表明：当虚警率为10

-3

时，该方法对弱小目标的平均检测概率为95.17%。基本满足红外弱小目标检测方法的稳定可靠、精度高等要求。

Abstract

To solve the problem of detecting small infrared targets ininfrared optical systems under complex backgrounds

an information processing model based on visual feature integration was established

and a dim small target detection method based on visual feature integration was proposed. First

the difference of Gaussians model of the receptive fields of retinal ganglion cells was used for processing the primary information of infrared images and initially detecting dim small targets. Then

the features containing the dim small targets were extracted by spatial and frequency channels. In the spatial channel

the second-order differential Hessian matrix was constructed with image information

and the local extremum was determined by calculating the basis truth and determinantto extract the structural component features. In the frequency domain channel

wavelets were used for decomposing the image frequency domain to extract the features of high-frequency components containing dim small targets. Finally

the spatial channel and frequency channel were integrated to extract the dim small targets from complex backgrounds. Experimental results indicate that when the false alarm rate is 10

-3

the average detection probability is 95.17%. The proposed method can basically meet the requirements of stability

reliability

and high accuracy.

关键词

Keywords

references

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