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View all- Ma L(2021)SAR Target Recognition Using Improved Sparse Representation with Local ReconstructionScientific Programming10.1155/2021/24468482021Online publication date: 1-Jan-2021
SAR Target Recognition Based on Joint Sparse Representation of Complementary Features
Pages 29 - 34
Abstract
This paper proposed a Synthetic Aperture Radar (SAR) target recognition method based on joint sparse representation of three complementary features. The Elliptical Fourier descriptors (EFDs) of the target outline and PCA features were extracted to depict the geometrical shape and intensity distribution of original SAR image. The azimuthal sensitivity image was constructed to describe the electromagnetic scattering characteristics of the target. The joint sparse representation was used to jointly classify the three features to exploit their complementary advantages. Finally, the target label of the test sample was decided based on the reconstruction errors. To validate the effeteness of the proposed method, experiments were conducted on the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset under various operating conditions.
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Index Terms
- SAR Target Recognition Based on Joint Sparse Representation of Complementary Features
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Published In
October 2018
88 pages
ISBN:9781450366205
DOI:10.1145/3290589
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Published: 12 October 2018
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SSIP 2018
SSIP 2018: 2018 International Conference on Sensors, Signal and Image Processing
October 12 - 14, 2018
Prague, Czech Republic
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View all- Ma L(2021)SAR Target Recognition Using Improved Sparse Representation with Local ReconstructionScientific Programming10.1155/2021/24468482021Online publication date: 1-Jan-2021
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