Infrared small target detection based on an image-patch tensor model

被引:39
作者
Zhang, Xiangyue [1 ,2 ,3 ,4 ,5 ]
Ding, Qinghai [6 ]
Luo, Haibo [1 ,4 ,5 ]
Hui, Bin [1 ,4 ,5 ]
Chang, Zheng [1 ,4 ,5 ]
Zhang, Junchao [1 ,2 ,3 ,4 ,5 ]
机构
[1] Chinese Acad Sci, Shenyang Inst Automat, Shenyang 110016, Liaoning, Peoples R China
[2] Chinese Acad Sci, Inst Robot & Intelligent Mfg, Shenyang 110000, Liaoning, Peoples R China
[3] Univ Chinese Acad Sci, Beijing 100049, Peoples R China
[4] Chinese Acad Sci, Key Lab Optoelect Informat Proc, Shenyang 110016, Liaoning, Peoples R China
[5] Key Lab Image Understanding & Comp Vis, Shenyang 110016, Liaoning, Peoples R China
[6] Space Star Technol Co Ltd, Beijing 100086, Peoples R China
关键词
Small target detection; Image patch; Tensor; Sparsity; Infrared image; ALGORITHM;
D O I
10.1016/j.infrared.2019.03.009
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
Infrared small target detection under complex background has been applied to many fields and is still a challenging problem. In this paper, a small target detection method base on an image-patch tensor (IPT) model is proposed. Firstly, considering the structural relationship between pixels, the original single-frame image is constructed as a new image-patch tensor. Secondly, based on the correlation of the background image patch and the sparsity of the target image patch, the small target detection problem can be transformed into an optimization problem of separating the low-rank part and the sparse part of the tensor. Finally, after simple post filtering, the target is separated adaptively. Experimental results show that the proposed method can detect the small target precisely and can keep a higher signal-to-clutter ratio (SCR). What's more, compared with other methods, the proposed method shows better detection performance.
引用
收藏
页码:55 / 63
页数:9
相关论文
共 28 条
[1]  
[Anonymous], 1993, Proc. SPIE
[2]  
[Anonymous], FOUND TRENDS MACH LE
[3]   Robust Principal Component Analysis? [J].
Candes, Emmanuel J. ;
Li, Xiaodong ;
Ma, Yi ;
Wright, John .
JOURNAL OF THE ACM, 2011, 58 (03)
[4]   A Local Contrast Method for Small Infrared Target Detection [J].
Chen, C. L. Philip ;
Li, Hong ;
Wei, Yantao ;
Xia, Tian ;
Tang, Yuan Yan .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2014, 52 (01) :574-581
[5]  
Dai Y., 2012, INFRARED PHYS TECHN, V77, P421
[6]   Small Infrared Target Detection Based on Weighted Local Difference Measure [J].
Deng, He ;
Sun, Xianping ;
Liu, Maili ;
Ye, Chaohui ;
Zhou, Xin .
IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 2016, 54 (07) :4204-4214
[7]   Max-Mean and Max-Median filters for detection of small-targets [J].
Deshpande, SD ;
Er, MH ;
Ronda, V ;
Chan, P .
SIGNAL AND DATA PROCESSING OF SMALL TARGETS 1999, 1999, 3809 :74-83
[8]   Infrared small-dim target detection based on Markov random field guided noise modeling [J].
Gao, Chenqiang ;
Wang, Lan ;
Xiao, Yongxing ;
Zhao, Qian ;
Meng, Deyu .
PATTERN RECOGNITION, 2018, 76 :463-475
[9]   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
[10]   A Kernel-Based Nonparametric Regression Method for Clutter Removal in Infrared Small-Target Detection Applications [J].
Gu, Yanfeng ;
Wang, Chen ;
Liu, BaoXue ;
Zhang, Ye .
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS, 2010, 7 (03) :469-473