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SAR target configuration recognition via structure preserving dictionary learning.

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Title: SAR target configuration recognition via structure preserving dictionary learning.
Authors: Liu, Ming1,2, Chen, Shichao3 rice0309@163.com, Lu, Fugang3, Wang, Jun3
Source: AEU: International Journal of Electronics & Communications. Jan2018, Vol. 83, p523-532. 10p.
Subjects: Synthetic aperture radar, Scattering potentials, Factorization, Machine learning, Algorithms
Abstract: Learned dictionaries have been validated to perform better than predefined ones in many application areas. Focusing on synthetic aperture radar (SAR) images, a structure preserving dictionary learning (SPDL) algorithm, which can capture and preserve the local and distant structures of the datasets for SAR target configuration recognition is proposed in this paper. Due to the target aspect angle sensitivity characteristic of SAR images, two structure preserving factors are embedded into the proposed SPDL algorithm. One is constructed to preserve the local structure of the datasets, and the other one is established to preserve the distant structure of the datasets. Both the local and distant structures of the datasets are preserved using the learned dictionary to realize target configuration recognition. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) database demonstrate that the proposed algorithm is capable of handling the situations with limited number of training samples and under noise conditions. [ABSTRACT FROM AUTHOR]
Copyright of AEU: International Journal of Electronics & Communications is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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Header DbId: egs
DbLabel: Engineering Source
An: 126535931
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: SAR target configuration recognition via structure preserving dictionary learning.
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  Data: <searchLink fieldCode="AR" term="%22Liu%2C+Ming%22">Liu, Ming</searchLink><relatesTo>1,2</relatesTo><br /><searchLink fieldCode="AR" term="%22Chen%2C+Shichao%22">Chen, Shichao</searchLink><relatesTo>3</relatesTo><i> rice0309@163.com</i><br /><searchLink fieldCode="AR" term="%22Lu%2C+Fugang%22">Lu, Fugang</searchLink><relatesTo>3</relatesTo><br /><searchLink fieldCode="AR" term="%22Wang%2C+Jun%22">Wang, Jun</searchLink><relatesTo>3</relatesTo>
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  Data: <searchLink fieldCode="JN" term="%22AEU%3A+International+Journal+of+Electronics+%26+Communications%22">AEU: International Journal of Electronics & Communications</searchLink>. Jan2018, Vol. 83, p523-532. 10p.
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  Data: <searchLink fieldCode="DE" term="%22Synthetic+aperture+radar%22">Synthetic aperture radar</searchLink><br /><searchLink fieldCode="DE" term="%22Scattering+potentials%22">Scattering potentials</searchLink><br /><searchLink fieldCode="DE" term="%22Factorization%22">Factorization</searchLink><br /><searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br /><searchLink fieldCode="DE" term="%22Algorithms%22">Algorithms</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Learned dictionaries have been validated to perform better than predefined ones in many application areas. Focusing on synthetic aperture radar (SAR) images, a structure preserving dictionary learning (SPDL) algorithm, which can capture and preserve the local and distant structures of the datasets for SAR target configuration recognition is proposed in this paper. Due to the target aspect angle sensitivity characteristic of SAR images, two structure preserving factors are embedded into the proposed SPDL algorithm. One is constructed to preserve the local structure of the datasets, and the other one is established to preserve the distant structure of the datasets. Both the local and distant structures of the datasets are preserved using the learned dictionary to realize target configuration recognition. Experimental results on the moving and stationary target acquisition and recognition (MSTAR) database demonstrate that the proposed algorithm is capable of handling the situations with limited number of training samples and under noise conditions. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Group: Ab
  Data: <i>Copyright of AEU: International Journal of Electronics & Communications is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1016/j.aeue.2017.11.001
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      – Code: eng
        Text: English
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        PageCount: 10
        StartPage: 523
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      – SubjectFull: Synthetic aperture radar
        Type: general
      – SubjectFull: Scattering potentials
        Type: general
      – SubjectFull: Factorization
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Algorithms
        Type: general
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      – TitleFull: SAR target configuration recognition via structure preserving dictionary learning.
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            – D: 01
              M: 01
              Text: Jan2018
              Type: published
              Y: 2018
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              Value: 83
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