Block Sparse Bayesian Learning over Local Dictionary for Robust SAR Target Recognition
Joint Authors
Source
International Journal of Optics
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-10, 10 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-08-01
Country of Publication
Egypt
No. of Pages
10
Main Subjects
Abstract EN
This paper applied block sparse Bayesian learning (BSBL) to synthetic aperture radar (SAR) target recognition.
The traditional sparse representation-based classification (SRC) operates on the global dictionary collaborated by different classes.
Afterwards, the similarities between the test sample and various classes are evaluated by the reconstruction errors.
This paper reconstructs the test sample based on local dictionaries formed by individual classes.
Considering the azimuthal sensitivity of SAR images, the linear coefficients on the local dictionary are sparse ones with block structure.
Therefore, to solve the sparse coefficients, the BSBL is employed.
The proposed method can better exploit the representation capability of each class, thus benefiting the recognition performance.
Based on the experimental results on the moving and stationary target acquisition and recognition (MSTAR) dataset, the effectiveness and robustness of the proposed method is confirmed.
American Psychological Association (APA)
Li, Chenyu& Liu, Guohua. 2020. Block Sparse Bayesian Learning over Local Dictionary for Robust SAR Target Recognition. International Journal of Optics،Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1172957
Modern Language Association (MLA)
Li, Chenyu& Liu, Guohua. Block Sparse Bayesian Learning over Local Dictionary for Robust SAR Target Recognition. International Journal of Optics No. 2020 (2020), pp.1-10.
https://search.emarefa.net/detail/BIM-1172957
American Medical Association (AMA)
Li, Chenyu& Liu, Guohua. Block Sparse Bayesian Learning over Local Dictionary for Robust SAR Target Recognition. International Journal of Optics. 2020. Vol. 2020, no. 2020, pp.1-10.
https://search.emarefa.net/detail/BIM-1172957
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1172957