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A Spatial–Temporal Block-Matching Patch-Tensor Model for Infrared Small Moving Target Detection in Complex Scenes
Detecting infrared (IR) small moving targets in complex scenes quickly, accurately, and robustly remains a challenging problem in the current research field. To address this issue, this paper proposes a novel spatial–temporal block-matching patch-tensor (STBMPT) model based on a low-rank sparse deco...
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| Published in: | Remote sensing (Basel, Switzerland) Switzerland), 2023-09, Vol.15 (17), p.4316 |
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| container_issue | 17 |
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| creator | Aliha, Aersi Liu, Yuhan Ma, Yapeng Hu, Yuxin Pan, Zongxu Zhou, Guangyao |
| description | Detecting infrared (IR) small moving targets in complex scenes quickly, accurately, and robustly remains a challenging problem in the current research field. To address this issue, this paper proposes a novel spatial–temporal block-matching patch-tensor (STBMPT) model based on a low-rank sparse decomposition (LRSD) framework. This model enhances the traditional infrared patch-tensor (IPT) model by incorporating joint spatial–temporal sampling to exploit inter-frame information and constructing a low-rank patch tensor using image block matching. Furthermore, a novel prior-weight calculation is introduced, utilizing the eigenvalues of the local structure tensor to suppress interference such as strong edges, corners, and point-like noise from the background. To improve detection efficiency, the tensor is constructed using a matching group instead of using a traditional sliding window. Finally, the background and target components are separated using the alternating direction method of multipliers (ADMM). Qualitative and quantitative experimental analysis in various scenes demonstrates the superior detection performance and efficiency of the proposed algorithm for detecting infrared small moving targets in complex scenes. |
| doi_str_mv | 10.3390/rs15174316 |
| format | article |
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To address this issue, this paper proposes a novel spatial–temporal block-matching patch-tensor (STBMPT) model based on a low-rank sparse decomposition (LRSD) framework. This model enhances the traditional infrared patch-tensor (IPT) model by incorporating joint spatial–temporal sampling to exploit inter-frame information and constructing a low-rank patch tensor using image block matching. Furthermore, a novel prior-weight calculation is introduced, utilizing the eigenvalues of the local structure tensor to suppress interference such as strong edges, corners, and point-like noise from the background. To improve detection efficiency, the tensor is constructed using a matching group instead of using a traditional sliding window. Finally, the background and target components are separated using the alternating direction method of multipliers (ADMM). Qualitative and quantitative experimental analysis in various scenes demonstrates the superior detection performance and efficiency of the proposed algorithm for detecting infrared small moving targets in complex scenes.</description><identifier>ISSN: 2072-4292</identifier><identifier>EISSN: 2072-4292</identifier><identifier>DOI: 10.3390/rs15174316</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Accuracy ; Algorithms ; Approximation ; Background noise ; complex scenes ; Deep learning ; Efficiency ; Eigenvalues ; False alarms ; Hypothesis testing ; image block matching ; infrared small moving target detection ; local prior weights ; Matching ; Mathematical analysis ; Methods ; Moving targets ; Neural networks ; Qualitative analysis ; Remote sensing ; Sparsity ; Target detection ; Tensors ; Unmanned aerial vehicles</subject><ispartof>Remote sensing (Basel, Switzerland), 2023-09, Vol.15 (17), p.4316</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c400t-5d3e30ce2175479e34bfcd9cb13416c64190c37e867b6185b4f19da52d3bedf23</citedby><cites>FETCH-LOGICAL-c400t-5d3e30ce2175479e34bfcd9cb13416c64190c37e867b6185b4f19da52d3bedf23</cites><orcidid>0000-0002-6703-6820 ; 0009-0004-9816-3146 ; 0000-0003-4328-6427 ; 0000-0002-5041-3300</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2862730653/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2862730653?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,44590,75126</link.rule.ids></links><search><creatorcontrib>Aliha, Aersi</creatorcontrib><creatorcontrib>Liu, Yuhan</creatorcontrib><creatorcontrib>Ma, Yapeng</creatorcontrib><creatorcontrib>Hu, Yuxin</creatorcontrib><creatorcontrib>Pan, Zongxu</creatorcontrib><creatorcontrib>Zhou, Guangyao</creatorcontrib><title>A Spatial–Temporal Block-Matching Patch-Tensor Model for Infrared Small Moving Target Detection in Complex Scenes</title><title>Remote sensing (Basel, Switzerland)</title><description>Detecting infrared (IR) small moving targets in complex scenes quickly, accurately, and robustly remains a challenging problem in the current research field. To address this issue, this paper proposes a novel spatial–temporal block-matching patch-tensor (STBMPT) model based on a low-rank sparse decomposition (LRSD) framework. This model enhances the traditional infrared patch-tensor (IPT) model by incorporating joint spatial–temporal sampling to exploit inter-frame information and constructing a low-rank patch tensor using image block matching. Furthermore, a novel prior-weight calculation is introduced, utilizing the eigenvalues of the local structure tensor to suppress interference such as strong edges, corners, and point-like noise from the background. To improve detection efficiency, the tensor is constructed using a matching group instead of using a traditional sliding window. Finally, the background and target components are separated using the alternating direction method of multipliers (ADMM). Qualitative and quantitative experimental analysis in various scenes demonstrates the superior detection performance and efficiency of the proposed algorithm for detecting infrared small moving targets in complex scenes.</description><subject>Accuracy</subject><subject>Algorithms</subject><subject>Approximation</subject><subject>Background noise</subject><subject>complex scenes</subject><subject>Deep learning</subject><subject>Efficiency</subject><subject>Eigenvalues</subject><subject>False alarms</subject><subject>Hypothesis testing</subject><subject>image block matching</subject><subject>infrared small moving target detection</subject><subject>local prior weights</subject><subject>Matching</subject><subject>Mathematical analysis</subject><subject>Methods</subject><subject>Moving targets</subject><subject>Neural networks</subject><subject>Qualitative analysis</subject><subject>Remote sensing</subject><subject>Sparsity</subject><subject>Target 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Spatial–Temporal Block-Matching Patch-Tensor Model for Infrared Small Moving Target Detection in Complex Scenes</title><author>Aliha, Aersi ; Liu, Yuhan ; Ma, Yapeng ; Hu, Yuxin ; Pan, Zongxu ; Zhou, Guangyao</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c400t-5d3e30ce2175479e34bfcd9cb13416c64190c37e867b6185b4f19da52d3bedf23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Accuracy</topic><topic>Algorithms</topic><topic>Approximation</topic><topic>Background noise</topic><topic>complex scenes</topic><topic>Deep learning</topic><topic>Efficiency</topic><topic>Eigenvalues</topic><topic>False alarms</topic><topic>Hypothesis testing</topic><topic>image block matching</topic><topic>infrared small moving target detection</topic><topic>local prior weights</topic><topic>Matching</topic><topic>Mathematical analysis</topic><topic>Methods</topic><topic>Moving 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To address this issue, this paper proposes a novel spatial–temporal block-matching patch-tensor (STBMPT) model based on a low-rank sparse decomposition (LRSD) framework. This model enhances the traditional infrared patch-tensor (IPT) model by incorporating joint spatial–temporal sampling to exploit inter-frame information and constructing a low-rank patch tensor using image block matching. Furthermore, a novel prior-weight calculation is introduced, utilizing the eigenvalues of the local structure tensor to suppress interference such as strong edges, corners, and point-like noise from the background. To improve detection efficiency, the tensor is constructed using a matching group instead of using a traditional sliding window. Finally, the background and target components are separated using the alternating direction method of multipliers (ADMM). 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| subjects | Accuracy Algorithms Approximation Background noise complex scenes Deep learning Efficiency Eigenvalues False alarms Hypothesis testing image block matching infrared small moving target detection local prior weights Matching Mathematical analysis Methods Moving targets Neural networks Qualitative analysis Remote sensing Sparsity Target detection Tensors Unmanned aerial vehicles |
| title | A Spatial–Temporal Block-Matching Patch-Tensor Model for Infrared Small Moving Target Detection in Complex Scenes |
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