红外弱小目标的分割预检测

靳永亮; 王延杰; 刘艳滢; 黄继鹏

doi:10.3788/OPE.20122001.0171

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红外弱小目标的分割预检测

信息科学 | 更新时间：2020-08-12

- 红外弱小目标的分割预检测
- Pre-detection method for small infrared target
- 光学精密工程 2012年20卷第1期页码：171-178
- 作者机构：
  
  1. 中国科学院长春光学精密机械与物理研究所,吉林长春,中国,130033
  2. 中国科学院研究生院北京,100039
- 作者简介：
- 基金信息：
  
  国家自然科学基金资助项目(No.60902067)()
- DOI：10.3788/OPE.20122001.0171
  中图分类号： TP391.4
- 收稿日期：2011-05-23，
  
  修回日期：2011-06-21，
  
  网络出版日期：2012-01-25，
  
  纸质出版日期：2012-01-25
- 稿件说明：
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靳永亮, 王延杰, 刘艳滢, 黄继鹏. 红外弱小目标的分割预检测[J]. 光学精密工程, 2012,20(1): 171-178

JIN Yong-liang, WANG Yan-jie, LIU Yan-ying, HUANG Ji-peng. Pre-detection method for small infrared target[J]. Editorial Office of Optics and Precision Engineering, 2012,20(1): 171-178
靳永亮, 王延杰, 刘艳滢, 黄继鹏. 红外弱小目标的分割预检测[J]. 光学精密工程, 2012,20(1): 171-178 DOI： 10.3788/OPE.20122001.0171.

JIN Yong-liang, WANG Yan-jie, LIU Yan-ying, HUANG Ji-peng. Pre-detection method for small infrared target[J]. Editorial Office of Optics and Precision Engineering, 2012,20(1): 171-178 DOI： 10.3788/OPE.20122001.0171.

摘要

提出了一种目标分割预检测方法来提高检测红外弱小目标的准确性和实时性。针对红外图像的特点

利用改进的自适应背景感知算法抑制目标图像的背景以提高目标检测概率;根据已有的先验知识构造属性集

把灰度直方图限定在感兴趣区域

减少背景的影响;然后

利用属性直方图的最大熵进行图像分割以检测目标。为了提高分割算法运算速度

应用了快速递推算法。实验结果表明

本文提出的背景抑制算法能更好地抑制背景

提高图像的整体信噪比;分割算法具有更好的分割检测效果

候选目标点分割准确、虚警目标点较少

运算速度提高了91%。对分割图像进行后续处理

剔除了大部分虚警目标点

为后续目标准确检测提供了有力保障。

Abstract

A segmentation and detection method for small infrared targets is proposed to improve the accuracy of target detection. Aiming at the characters of an infrared image

an improved background perception algorithm is used to suppress backgrounds for increasing the target detection probability

and the bound set is constructed to limit the gray level histogram into a Region of Interest (ROI) to reduce the interference of background. Then

target detection is achieved through image segmentation using the maximum entropy of a 2D bound histogram. Furthermore

the fast recurring algorithm is applied to the proposed algorithm for accelerating the running speed of the segmentation algorithm. Experiment results show that proposed background suppression algorithm has the better performance in background suppression and can improve the signal to Noise Ratio (SNR) of the image. Moreover

the segmentation algorithm shows a better effectiveness in target segmentation and detection

and its candidate target is separated more accurate with less false alarm points and running speed has improved by 91%. Through post-process for the image

most of the false alarm points are eliminated

which provides powerful guarantee for the subsequent accurate detection.

关键词

Keywords

references

刘兴淼, 王仕成, 赵静. 结合统计分布和非下采样Contourlet变换的红外弱小目标检测 [J]. 光学精密工程, 2011, 19(4): 908-915. LIU X M, WANG SH CH, ZHAO J. Infrared small target detection based on nonsubsampled Contourlet transform and statistical distribution [J]. Opt. Precision Eng., 2011, 19(4): 908-915. (in Chinese)[2] 曾明, 李建勋. 基于自适应形态学Top-Hat滤波器的红外弱小目标检测方法 [J].上海交通大学学报,2006,40(1):90-93. ZENG M, LI J X. The small target detection in infrared image based on adaptive morphological Top-Hat filter [J]. Journal of Shanghai Jiaotong University, 2006, 40(1):90-93.(in Chinese)[3] 李欣,赵亦工. 基于模糊分类的弱小目标检测方法 [J].光学精密工程,2009,17(9):2312-2320. LI X, ZHAO Y G. Approach to dim and small target detection based on fuzzy classification [J]. Opt. Precision Eng., 2009, 17(9):2312-2320. (in Chinese)[4] 余农, 吴常泳, 汤心溢,等. 红外目标检测的自适应背景感知算法 [J].电子学报,2005,33(2):200-204. YU N, WU CH Y, TANG X Y, et al.. Adaptive background perception algorithm for infrared target detection[J]. Acta Electronica Sinica, 2007, 2(1): 201-204. (in Chinese)[5] JACKWAY P T. Improved morphological Top-Hat [J]. IEEE Electronic Letters, 2000, 14(6): 1194-1195.[6] 周洪武,朱兆达,吴一全,等. 基于TOP-HAT算子滤波器算子的红外弱小目标检测算法 [J].南京航空航天大学学报,2007,39(2):213-216. ZHOU H W, ZHU ZH D, WU Y Q, et al.. Optimized design of improved TOP-HAT filter based on genetic algorithms of neural network [J]. Journal of Nanjing University of Aeronautics & Astronautics, 2007, 39(2):213-216.(in Chinese)[7] 郭海涛,田坦. 利用二维属性直方图的最大熵的图像分割方法 [J].光学学报,2006,26(4):506-509. GUO H T, TIAN T. Image segmentation using the maximum entropy of the two-dimensional bound histogram[J]. Acta Optica Sinica, 2006, 26(4): 506-509. (in Chinese)[8] 汪海洋,潘德炉. 二维Otsu自适应阈值选取的快速实现 [J].自动化学报,2007,33(9):968-971. WANG H Y, PAN D L. A fast algorithm for two-dimensional Otsu adaptive threshold algorithm[J]. Acta Automatica Sinica, 2006, 33(9):968-971. (in Chinese)[9] 李欣, 赵亦工, 郭伟. 基于复杂度的自适应门限弱小目标检测 [J]. 光子学报, 2009, 38(8): 2144-2149. LI X, ZHAO Y G, GUO W. Adaptive threshold detection method for dim and small target based on image complex degree[J]. Acta Photonica Sinica, 2009, 38(8): 2144-2149.(in Chinese)[10] 彭嘉雄, 周文琳. 红外背景抑制与小目标分割检测 [J]. 电子学报, 1999,27(12): 47-51. PENG J X, ZHOU W L. Infrared background suppression for segmenting and detecting small target[J]. Acta Electronica Sinica, 1999, 27(12): 47-51. (in Chinese)

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