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Content Provider | IET Digital Library |
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Author | Yu, Meiting Zhang, Siqian Dong, Ganggang Zhao, Lingjun Kuang, Gangyao |
Abstract | Extracting valuable and discriminative features is one of the crucial issues for target recognition in synthetic aperture radar (SAR) images. In this study, a feature extraction method based on robust locality discriminant projection (RLDP) is presented for SAR target recognition. To characterise the local structural information of SAR images, the manifold learning technique called the supervised locality preserving projection is introduced to learn a linear projection, with which the SAR image can be cast into an implicit feature space. Then, the authors extend t-distributed stochastic neighbour embedding to a parametric framework for optimising the linear projection. In the resulting feature space, the intrinsic neighbour relation with a certain class can be preserved. In addition, the separation between different classes can be enhanced. Unlike most local manifold learning methods, the proposed method is robust to changes of the neighbour parameter. To further analyse the non-linear structure, a useful variant of RLDP named kernel RLDP (KRLDP) is proposed. KRLDP exploits RLDP in an implicit reproducing kernel Hilbert space, where the kernel-based non-linear projection is learned to capture the non-linear structural information. Extensive experiments on moving and stationary target automatic recognition databases demonstrate the effectiveness of the proposed methods. |
Starting Page | 1285 |
Ending Page | 1293 |
Page Count | 9 |
ISSN | 17518784 |
Volume Number | 12 |
e-ISSN | 17518792 |
Issue Number | Issue 11, Nov (2018) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-rsn/12/11 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-rsn.2018.5132 |
Journal | IET Radar, Sonar & Navigation |
Publisher Date | 2018-07-20 |
Access Restriction | Open |
Rights Holder | © The Institution of Engineering and Technology |
Subject Keyword | Computer Vision And Image Processing Technique Discriminative Features Feature Extraction Geophysical Image Processing Hilbert Space Image Classification Image Recognition Image Representation Implicit Feature Space Implicit Reproducing Kernel Hilbert Space Kernel-based Nonlinear Projection Knowledge Engineering Technique Learning in AI Linear Projection Local Manifold Learning Method Local Structural Information Manifold Learning Technique Moving Stationary Target Automatic Recognition Database Nonlinear Structural Information Nonlinear Structure Optical, Image And Video Signal Processing Radar Imaging Resulting Feature Space RLDP Robust Locality Discriminant Projection SAR Image SAR Target Recognition Statistics Supervised Locality Preserving Projection Synthetic Aperture Radar Synthetic Aperture Radar Image Valuable Feature |
Content Type | Text |
Resource Type | Article |
Subject | Electrical and Electronic Engineering |
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