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Remote Sens. 2019, 11(4), 382; https://doi.org/10.3390/rs11040382

Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm

1
School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China
2
Center for Information Geoscience, University of Electronic Science and Technology of China, Chengdu 611731, China
*
Author to whom correspondence should be addressed.
Received: 12 January 2019 / Revised: 4 February 2019 / Accepted: 11 February 2019 / Published: 13 February 2019
(This article belongs to the Special Issue Remote Sensing for Target Object Detection and Identification)
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Abstract

Excellent performance, real time and strong robustness are three vital requirements for infrared small target detection. Unfortunately, many current state-of-the-art methods merely achieve one of the expectations when coping with highly complex scenes. In fact, a common problem is that real-time processing and great detection ability are difficult to coordinate. Therefore, to address this issue, a robust infrared patch-tensor model for detecting an infrared small target is proposed in this paper. On the basis of infrared patch-tensor (IPT) model, a novel nonconvex low-rank constraint named partial sum of tensor nuclear norm (PSTNN) joint weighted l1 norm was employed to efficiently suppress the background and preserve the target. Due to the deficiency of RIPT which would over-shrink the target with the possibility of disappearing, an improved local prior map simultaneously encoded with target-related and background-related information was introduced into the model. With the help of a reweighted scheme for enhancing the sparsity and high-efficiency version of tensor singular value decomposition (t-SVD), the total algorithm complexity and computation time can be reduced dramatically. Then, the decomposition of the target and background is transformed into a tensor robust principle component analysis problem (TRPCA), which can be efficiently solved by alternating direction method of multipliers (ADMM). A series of experiments substantiate the superiority of the proposed method beyond state-of-the-art baselines. View Full-Text
Keywords: infrared small target detection; local prior analysis; nonconvex tensor robust principle component analysis; partial sum of the tensor nuclear norm infrared small target detection; local prior analysis; nonconvex tensor robust principle component analysis; partial sum of the tensor nuclear norm
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Zhang, L.; Peng, Z. Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm. Remote Sens. 2019, 11, 382.

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