基于奇异值分解和叠加法的慢速小目标检测算法

摘要/Abstract

摘要：

摘要：在图像集同一像素位置噪声满足遍历性的条件下，分析了利用叠加法进行目标检测的可行性，在此基础上提出了一种基于叠加法的慢速小目标检测算法.首先，分析了目标不重叠、完全重叠和部分重叠时，目标合成图像和噪声合成图像的能量变化情况，分析表明随着叠加次数的增加，噪声合成图像的能量衰减快于目标合成图像，从而保证了合成图像中目标与噪声能量比的增加，为利用叠加法进行目标检测提供了可行性.其次，根据合成图像的奇异值特性，利用标准化奇异值的不均匀变化完成了目标检测.同时，仿真验证了目标合成图像和噪声合成图像的能量衰减情况，分析了叠加次数、目标尺寸与强度对目标可检测性的影响.

关键词: 奇异值分解, 叠加法, 目标检测

Abstract:

Abstract： In the case of the measurement noises obtained from the same pixel position in a set of images which are ergodic, the feasibility of superposition method for target detection was analyzed, based on which, a superposition-based lowspeed small target detection algorithm was proposed. First, the energy variation of the target synthesis images and the noise synthesis images were analysized, when the targets were non-overlapped, completely overlapped and partially overlapped. The analysis shows that with the increase in the superimposed number, the energy of the noise synthesis images is attenuated faster than that of the target synthesis images, thus ensuring an increase in the ratio of target to noise in synthesis images. Second, depending on the characteristics of singular values of the synthesis images, the uneven variations of the standardized singular values were used for target detection. The simulation verifies the attenuation of the target energy and noise energy of the synthesis images, and analyzes the impact of superimposed number， target size and strength on target detectability.

Key words: singular value decomposition (SVD), superposition, object detection

中图分类号:

TP 391

高敬礼1，文成林2，刘妹琴1. 基于奇异值分解和叠加法的慢速小目标检测算法[J]. 上海交通大学学报（自然版）, 2015, 49(06): 876-883.

GAO Jingli1，WEN Chenglin2，LIU Meiqin1. Low-Speed Small Target Detection Based on SVD and Superposition[J]. Journal of Shanghai Jiaotong University, 2015, 49(06): 876-883.

参考文献

［1］张建伟, 郭会明. 低空慢速小目标拦截系统研究［J］. 计算机工程与设计, 2012, 33(7): 28742878.

Jianwei Z, Huiming G. Net cast interception system research aimed at low small slow target［J］. Computer Engineering and Design, 2012, 33(7):28742878.

［2］王忠, 马妍, 王莲荣. 防空作战中低慢小目标威胁度评估［J］. 舰船电子对抗, 2013, 36(6): 103105.

WANG Zhong, MA Yan, WANG Lianrong. Assessment of threat degree for LSS target in air defense operation［J］. Shipboard Electronic Countermeasure, 2013.

［3］Li Z Z, Chen J, Hou Q, et al. Sparse representation for infrared dim target detection via a discriminative overcomplete dictionary learned online［J］. Sensors, 2014, 14(6): 94519470.

［4］Zhao J, Liu F, Mo B. An algorithm of dim and small target detection based on wavelet transform and image fusion［C］∥2012 Fifth International Symposium on Computational Intelligence and Design (ISCID). USA: IEEE, 2012, 2: 4345.

［5］臧传吉, 李桂祥, 王宇翔. 基于形态学方法的红外小目标检测［J］. 空军预警学院学报, 2012(2):9497.

ZANG Chuanji,LI Guixiang,WANG Yuxiang. Infrared dim target detection using morphology based method［J］. Journal of Air Force Radar Academy, 2012,26(2):9497.

［6］Gao J L, Wen C L, Liu M Q. steel surface defect detection and localization based on SVD and twoside compressive measurements［C］∥26th Chinese Control and Decision Conference. USA: IEEE, 2014: 14011406.

［7］Lu C J, Tsai D M. Automatic defect inspection for LCDs using singular value decomposition［J］. The International Journal of Advanced Manufacturing Technology, 2005, 25(12): 5361.

［8］Reed I S, Gagliardi R M, Stotts L B. Optical moving target detection with 3D matched filtering［J］. IEEE Transactions on Aerospace and Electronic Systems, 1988, 24(4): 327336.

［9］Blostein S D, Huang T S. Detecting small, moving objects in image sequences using sequential hypothesis testing［J］. IEEE Transactions on Signal Processing, 1991, 39(7): 16111629.

［10］Arnold J, Shaw S W, Pasternack H. Efficient target tracking using dynamic programming［J］. IEEE Transactions on Aerospace and Electronic Systems, 1993, 29(1): 4456.

［11］Falconer D G. Target tracking with the Hough transform［C］∥1977 11th Asilomar Conference on Circuits, Systems and Computers. USA: IEEE, 1977: 249252.

［12］Gao J L, Wen C L, Liu M Q. SVDbased scattered small targets detection［C］∥2014 International Conference on Multisensor Fusion and Information Integration for Intelligent Systems (MFI). USA: IEEE, 2014: 16.

［13］Petrou M, Petrou C. Image processing: The fundamentals［M］. Second Edition. United Kingdom: John Wiley and Sons, 2010.

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