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A Local Contrast Method for Small Infrared Target Detection

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成果类型:
期刊论文
作者:
Chen, C. L. Philip*;Li, Hong;Wei, Yantao;Xia, Tian;Tang, Yuan Yan
通讯作者:
Chen, C. L. Philip
作者机构:
[Chen, C. L. Philip] Univ Macau, Fac Sci & Technol, Dept Comp & Informat Sci, Macau, Peoples R China.
[Xia, Tian; Tang, Yuan Yan] Univ Macau, Fac Sci & Technol, Macau, Peoples R China.
[Li, Hong] Huazhong Univ Sci & Technol, Sch Math & Stat, Wuhan 430074, Peoples R China.
[Wei, Yantao] Cent China Normal Univ, Coll Informat Technol Journalism & Commun, Wuhan 430079, Peoples R China.
[Wei, Yantao] Huazhong Univ Sci & Technol, Inst Pattern Recognit & Artificial Intelligence, Wuhan 430074, Peoples R China.
通讯机构:
[Chen, C. L. Philip] U
Univ Macau, Fac Sci & Technol, Dept Comp & Informat Sci, Macau, Peoples R China.
语种:
英文
关键词:
Derived kernel (DK);infrared (IR) image;local contrast;signal-to-noise ratio (SNR);target detection
期刊:
IEEE Transactions on Geoscience and Remote Sensing
ISSN:
0196-2892
年:
2014
卷:
52
期:
1
页码:
574-581
基金类别:
National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [61075116]; Natural Science Foundation of Hubei ProvinceNatural Science Foundation of Hubei Province [2009CDB387]; Chinese National Basic Research 973 ProgramNational Basic Research Program of China [2011CB302800]; Macau Science and Technology Development Fund [008/2010/A1]; Multi-Year Research of University of Macau [MYRG205(Y1-L4)-FST11-TYY, MYRG187(Y1-L3)-FST11-TYY]; University of Macau [SRG010-FST11-TYY]
机构署名:
本校为其他机构
院系归属:
新闻传播学院
信息管理学院
摘要:
Robust small target detection of low signal-to-noise ratio (SNR) is very important in infrared search and track applications for self-defense or attacks. Consequently, an effective small target detection algorithm inspired by the contrast mechanism of human vision system and derived kernel model is presented in this paper. At the first stage, the local contrast map of the input image is obtained using the proposed local contrast measure which measures the dissimilarity between the current location and its neighborhoods. In this way, target signal enhancement and background clutter suppression are achieved simultaneously. At the second stage, an adaptive threshold is adopted to segment the target. The experiments on two sequences have validated the detection capability of the proposed target detection method. Experimental evaluation results show that our method is simple and effective with respect to detection accuracy. In particular, the proposed method can improve the SNR of the image significantly.

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