Feature Extraction Based on Non-Subsampled Shearlet Transform (NSST) with Application to SAR Image Data
Joint Authors
Source
Mathematical Problems in Engineering
Issue
Vol. 2020, Issue 2020 (31 Dec. 2020), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2020-11-21
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
Considering the defaults in synthetic aperture radar (SAR) image feature extraction, an SAR target recognition method based on non-subsampled Shearlet transform (NSST) was proposed with application to target recognition.
NSST was used to decompose an SAR image into multilevel representations.
These representations were translation-invariant, and they could well reflect the dominant and detailed properties of the target.
During the machine learning classification stage, the joint sparse representation was employed to jointly represent the multilevel representations.
The joint sparse representation could represent individual components independently while considering the inner correlations between different components.
Therefore, the precision of joint representation could be enhanced.
Finally, the target label of the test sample was determined according to the overall reconstruction error.
Experiments were conducted on the MSTAR dataset to examine the proposed method, and the results confirmed its validity and robustness under the standard operating condition, configuration variance, depression angle variance, and noise corruption.
American Psychological Association (APA)
Ding, Huijie& Lin, Arthur K. L.. 2020. Feature Extraction Based on Non-Subsampled Shearlet Transform (NSST) with Application to SAR Image Data. Mathematical Problems in Engineering،Vol. 2020, no. 2020, pp.1-6.
https://search.emarefa.net/detail/BIM-1201844
Modern Language Association (MLA)
Ding, Huijie& Lin, Arthur K. L.. Feature Extraction Based on Non-Subsampled Shearlet Transform (NSST) with Application to SAR Image Data. Mathematical Problems in Engineering No. 2020 (2020), pp.1-6.
https://search.emarefa.net/detail/BIM-1201844
American Medical Association (AMA)
Ding, Huijie& Lin, Arthur K. L.. Feature Extraction Based on Non-Subsampled Shearlet Transform (NSST) with Application to SAR Image Data. Mathematical Problems in Engineering. 2020. Vol. 2020, no. 2020, pp.1-6.
https://search.emarefa.net/detail/BIM-1201844
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1201844