Infrared patch-image model for small target detection in a single image

Gao, Chenqiang, Meng, Deyu, Yang, Yi, Wang, Yongtao, Zhou, Xiaofang and Hauptmann, Alexander G. (2013) Infrared patch-image model for small target detection in a single image. IEEE Transactions on Image Processing, 22 12: 4996-5009. doi:10.1109/TIP.2013.2281420

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Author Gao, Chenqiang
Meng, Deyu
Yang, Yi
Wang, Yongtao
Zhou, Xiaofang
Hauptmann, Alexander G.
Title Infrared patch-image model for small target detection in a single image
Journal name IEEE Transactions on Image Processing   Check publisher's open access policy
ISSN 1057-7149
Publication date 2013-12-01
Sub-type Article (original research)
DOI 10.1109/TIP.2013.2281420
Open Access Status Not yet assessed
Volume 22
Issue 12
Start page 4996
End page 5009
Total pages 14
Place of publication Piscataway, NJ, United States
Publisher Institute of Electrical and Electronics Engineers
Language eng
Abstract The robust detection of small targets is one of the key techniques in infrared search and tracking applications. A novel small target detection method in a single infrared image is proposed in this paper. Initially, the traditional infrared image model is generalized to a new infrared patch-image model using local patch construction. Then, because of the non-local self-correlation property of the infrared background image, based on the new model small target detection is formulated as an optimization problem of recovering low-rank and sparse matrices, which is effectively solved using stable principle component pursuit. Finally, a simple adaptive segmentation method is used to segment the target image and the segmentation result can be refined by post-processing. Extensive synthetic and real data experiments show that under different clutter backgrounds the proposed method not only works more stably for different target sizes and signal-to-clutter ratio values, but also has better detection performance compared with conventional baseline methods.
Keyword Infrared image
Small target detection
Low-rank matrix recovery
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Information Technology and Electrical Engineering Publications
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Citation counts: TR Web of Science Citation Count  Cited 77 times in Thomson Reuters Web of Science Article | Citations
Scopus Citation Count Cited 100 times in Scopus Article | Citations
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Created: Sun, 10 Nov 2013, 10:21:39 EST by System User on behalf of School of Information Technol and Elec Engineering