Small infrared target detection based on low-rank and sparse representation

被引：145
作者：
He, Yujie [1 ]
Li, Min [1 ]
Zhang, Jinli [1 ]
An, Qi [1 ]
机构：
[1] Xian Res Inst Hitech, Xian 710025, Peoples R China
来源：
INFRARED PHYSICS & TECHNOLOGY | 2015年 / 68卷
基金：
中国国家自然科学基金;
关键词：
Low rank and sparse representation; Low rank representation; Sparse representation; Infrared small target detection; FILTERS; DIM;
D O I：
10.1016/j.infrared.2014.10.022
中图分类号：
TH7 [仪器、仪表];
学科分类号：
0804 ; 080401 ; 081102 ;
摘要：
The method by which to obtain the correct detection result for infrared small targets is an important and challenging issue in infrared applications. In this paper, a low-rank and sparse representation (LRSR) model is proposed. This model can describe the specific structure of noise data effectively by utilizing sparse representation theory on the basis of low-rank matrix representation. In addition, LRSR based infrared small target detection algorithm is presented. First, a two-dimensional Gaussian model is used to produce the atoms that construct over-complete target dictionary. Then, the reset image data matrix is decomposed by the LRSR model to obtain the background, noise and target components of the image. Finally, the target position can be determined by threshold processing for the target component data. The experimental results in single objective frame, multi-objective image sequences, and strong noise background conditions demonstrate that the proposed method not only has high detection performance in effectively reducing the false alarm rate but also has strong robustness against noise interference. (C) 2014 Elsevier B.V. All rights reserved.
引用
收藏
页码：98 / 109
页数：12
相关论文
共 24 条
[11]   Infrared Patch-Image Model for Small Target Detection in a Single Image [J].
Gao, Chenqiang ;
Meng, Deyu ;
Yang, Yi ;
Wang, Yongtao ;
Zhou, Xiaofang ;
Hauptmann, Alexander G. .
IEEE TRANSACTIONS ON IMAGE PROCESSING, 2013, 22 (12) :4996-5009
[12]  
Hu T, 2010, J INFRARED MILLIM W, V29, P303
[13]   Small Target Detection Utilizing Robust Methods of the Human Visual System for IRST [J].
Kim, Sungho ;
Yang, Yukyung ;
Lee, Joohyoung ;
Park, Yongchan .
JOURNAL OF INFRARED MILLIMETER AND TERAHERTZ WAVES, 2009, 30 (09) :994-1011
[14]   Robust Recovery of Subspace Structures by Low-Rank Representation [J].
Liu, Guangcan ;
Lin, Zhouchen ;
Yan, Shuicheng ;
Sun, Ju ;
Yu, Yong ;
Ma, Yi .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2013, 35 (01) :171-184
[15]   Infrared Point Target Detection with Fisher Linear Discriminant and Kernel Fisher Linear Discriminant [J].
Liu, Ruiming ;
Zhi, Hongliang .
JOURNAL OF INFRARED MILLIMETER AND TERAHERTZ WAVES, 2010, 31 (12) :1491-1502
[16]   Detection of small objects in image data based on the nonlinear principal component analysis neural network [J].
Liu, ZJ ;
Chen, CY ;
Shen, XB ;
Zou, XC .
OPTICAL ENGINEERING, 2005, 44 (09)
[17]   An improved infrared dim and small target detection algorithm based on the contrast mechanism of human visual system [J].
Shao, Xiaopeng ;
Fan, Hua ;
Lu, Guangxu ;
Xu, Jun .
INFRARED PHYSICS & TECHNOLOGY, 2012, 55 (05) :403-408
[18]   Infrared small target detection using directional highpass filters based on LS-SVM [J].
Wang, P. ;
Tian, J. W. ;
Gao, Ch. Q. .
ELECTRONICS LETTERS, 2009, 45 (03) :156-158
[19]   Robust Face Recognition via Sparse Representation [J].
Wright, John ;
Yang, Allen Y. ;
Ganesh, Arvind ;
Sastry, S. Shankar ;
Ma, Yi .
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, 2009, 31 (02) :210-227
[20]   The design of Top-Hat morphological filter and application to infrared target detection [J].
Zeng, M ;
Li, JX ;
Peng, Z .
INFRARED PHYSICS & TECHNOLOGY, 2006, 48 (01) :67-76
123