Infrared Small and Moving Target Detection on Account of the Minimization of Non-Convex Spatial-Temporal Tensor Low-Rank Approximation under the Complex Background

Infrared point-target detection is one of the key technologies in infrared guidance systems. Due to the long observation distance, the point target is often submerged in the background clutter and large noise in the process of atmospheric transmission and scattering, and the signal-to-noise ratio is...

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Main Authors: Kun Wang, Defu Jiang, Lijun Yun, Xiaoyang Liu
Format: Article
Language:English
Published: MDPI AG 2023-01-01
Series:Applied Sciences
Subjects:
Online Access:https://www.mdpi.com/2076-3417/13/2/1196
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author Kun Wang
Defu Jiang
Lijun Yun
Xiaoyang Liu
author_facet Kun Wang
Defu Jiang
Lijun Yun
Xiaoyang Liu
author_sort Kun Wang
collection DOAJ
description Infrared point-target detection is one of the key technologies in infrared guidance systems. Due to the long observation distance, the point target is often submerged in the background clutter and large noise in the process of atmospheric transmission and scattering, and the signal-to-noise ratio is low. On the other hand, the target in the image appears in the form of fuzzy points, so that the target has no obvious features and texture information. Therefore, scholars have proposed many object detection methods for dimming infrared images, which has become a hot research topic on account of the flow-rank model based on the image patch. However, the result has a high false alarm rate because the most low-rank models based on the image patch do not consider the spatial-temporal characteristics of the infrared sequences. Therefore, we introduce 3D total variation (3D-TV) to regularize the foreground on account of the non-convex rank approximation minimization method, so as to consider the spatial-temporal continuity of the target and effectively suppress the interference caused by dynamic background and target movement on the foreground extraction. Finally, this paper proposes the minimization of the non-convex spatial-temporal tensor low-rank approximation algorithm (MNSTLA) by studying the related algorithms of the point infrared target detection, and the experimental results show strong robustness and a low false alarm rate for the proposed method compared with other advanced algorithms, such as NARM, RIPT, and WSNMSTIPT.
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spelling doaj.art-5a81516a83364a21a88102b353ca01fc2023-11-30T21:07:33ZengMDPI AGApplied Sciences2076-34172023-01-01132119610.3390/app13021196Infrared Small and Moving Target Detection on Account of the Minimization of Non-Convex Spatial-Temporal Tensor Low-Rank Approximation under the Complex BackgroundKun Wang0Defu Jiang1Lijun Yun2Xiaoyang Liu3College of Computer and Information, Hohai University, Nanjing 211000, ChinaCollege of Computer and Information, Hohai University, Nanjing 211000, ChinaSchool of Information Science and Technology, Yunnan Normal University, Kunming 650500, ChinaDepartment of Computer Science and Engineering, Chongqing University of Technology, Chongqing 400054, ChinaInfrared point-target detection is one of the key technologies in infrared guidance systems. Due to the long observation distance, the point target is often submerged in the background clutter and large noise in the process of atmospheric transmission and scattering, and the signal-to-noise ratio is low. On the other hand, the target in the image appears in the form of fuzzy points, so that the target has no obvious features and texture information. Therefore, scholars have proposed many object detection methods for dimming infrared images, which has become a hot research topic on account of the flow-rank model based on the image patch. However, the result has a high false alarm rate because the most low-rank models based on the image patch do not consider the spatial-temporal characteristics of the infrared sequences. Therefore, we introduce 3D total variation (3D-TV) to regularize the foreground on account of the non-convex rank approximation minimization method, so as to consider the spatial-temporal continuity of the target and effectively suppress the interference caused by dynamic background and target movement on the foreground extraction. Finally, this paper proposes the minimization of the non-convex spatial-temporal tensor low-rank approximation algorithm (MNSTLA) by studying the related algorithms of the point infrared target detection, and the experimental results show strong robustness and a low false alarm rate for the proposed method compared with other advanced algorithms, such as NARM, RIPT, and WSNMSTIPT.https://www.mdpi.com/2076-3417/13/2/1196complex backgroundinfrared imageMNSTLApoint target detection
spellingShingle Kun Wang
Defu Jiang
Lijun Yun
Xiaoyang Liu
Infrared Small and Moving Target Detection on Account of the Minimization of Non-Convex Spatial-Temporal Tensor Low-Rank Approximation under the Complex Background
Applied Sciences
complex background
infrared image
MNSTLA
point target detection
title Infrared Small and Moving Target Detection on Account of the Minimization of Non-Convex Spatial-Temporal Tensor Low-Rank Approximation under the Complex Background
title_full Infrared Small and Moving Target Detection on Account of the Minimization of Non-Convex Spatial-Temporal Tensor Low-Rank Approximation under the Complex Background
title_fullStr Infrared Small and Moving Target Detection on Account of the Minimization of Non-Convex Spatial-Temporal Tensor Low-Rank Approximation under the Complex Background
title_full_unstemmed Infrared Small and Moving Target Detection on Account of the Minimization of Non-Convex Spatial-Temporal Tensor Low-Rank Approximation under the Complex Background
title_short Infrared Small and Moving Target Detection on Account of the Minimization of Non-Convex Spatial-Temporal Tensor Low-Rank Approximation under the Complex Background
title_sort infrared small and moving target detection on account of the minimization of non convex spatial temporal tensor low rank approximation under the complex background
topic complex background
infrared image
MNSTLA
point target detection
url https://www.mdpi.com/2076-3417/13/2/1196
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AT lijunyun infraredsmallandmovingtargetdetectiononaccountoftheminimizationofnonconvexspatialtemporaltensorlowrankapproximationunderthecomplexbackground
AT xiaoyangliu infraredsmallandmovingtargetdetectiononaccountoftheminimizationofnonconvexspatialtemporaltensorlowrankapproximationunderthecomplexbackground