Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm
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 pro...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-02-01
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| Series: | Remote Sensing |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2072-4292/11/4/382 |
| _version_ | 1798030951980204032 |
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| author | Landan Zhang Zhenming Peng |
| author_facet | Landan Zhang Zhenming Peng |
| author_sort | Landan Zhang |
| collection | DOAJ |
| description | 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 <i>l</i><sub>1</sub> 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. |
| first_indexed | 2024-04-11T19:49:34Z |
| format | Article |
| id | doaj.art-b9f75c9bd45f4b1cbca7048fcf15e002 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T19:49:34Z |
| publishDate | 2019-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-b9f75c9bd45f4b1cbca7048fcf15e0022022-12-22T04:06:21ZengMDPI AGRemote Sensing2072-42922019-02-0111438210.3390/rs11040382rs11040382Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear NormLandan Zhang0Zhenming Peng1School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaSchool of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu 611731, ChinaExcellent 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 <i>l</i><sub>1</sub> 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.https://www.mdpi.com/2072-4292/11/4/382infrared small target detectionlocal prior analysisnonconvex tensor robust principle component analysispartial sum of the tensor nuclear norm |
| spellingShingle | Landan Zhang Zhenming Peng Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm Remote Sensing infrared small target detection local prior analysis nonconvex tensor robust principle component analysis partial sum of the tensor nuclear norm |
| title | Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm |
| title_full | Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm |
| title_fullStr | Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm |
| title_full_unstemmed | Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm |
| title_short | Infrared Small Target Detection Based on Partial Sum of the Tensor Nuclear Norm |
| title_sort | infrared small target detection based on partial sum of the tensor nuclear norm |
| topic | infrared small target detection local prior analysis nonconvex tensor robust principle component analysis partial sum of the tensor nuclear norm |
| url | https://www.mdpi.com/2072-4292/11/4/382 |
| work_keys_str_mv | AT landanzhang infraredsmalltargetdetectionbasedonpartialsumofthetensornuclearnorm AT zhenmingpeng infraredsmalltargetdetectionbasedonpartialsumofthetensornuclearnorm |