Next Article in Journal
Best Accuracy Land Use/Land Cover (LULC) Classification to Derive Crop Types Using Multitemporal, Multisensor, and Multi-Polarization SAR Satellite Images
Previous Article in Journal
Comparison of Four Machine Learning Methods for Generating the GLASS Fractional Vegetation Cover Product from MODIS Data
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(8), 683; doi:10.3390/rs8080683

SAR Target Recognition via Supervised Discriminative Dictionary Learning and Sparse Representation of the SAR-HOG Feature

1,2,* , 1
and
1
1
Department of Electronic Engineering, Tsinghua University, Beijing 100084, China
2
Luoyang Electronic Equipment Test Center, Luoyang 471000, China
*
Author to whom correspondence should be addressed.
Academic Editors: Xiaofeng Li and Prasad S. Thenkabail
Received: 25 May 2016 / Revised: 22 July 2016 / Accepted: 17 August 2016 / Published: 20 August 2016
View Full-Text   |   Download PDF [873 KB, uploaded 20 August 2016]   |  

Abstract

Automatic target recognition (ATR) in synthetic aperture radar (SAR) images plays an important role in both national defense and civil applications. Although many methods have been proposed, SAR ATR is still very challenging due to the complex application environment. Feature extraction and classification are key points in SAR ATR. In this paper, we first design a novel feature, which is a histogram of oriented gradients (HOG)-like feature for SAR ATR (called SAR-HOG). Then, we propose a supervised discriminative dictionary learning (SDDL) method to learn a discriminative dictionary for SAR ATR and propose a strategy to simplify the optimization problem. Finally, we propose a SAR ATR classifier based on SDDL and sparse representation (called SDDLSR), in which both the reconstruction error and the classification error are considered. Extensive experiments are performed on the MSTAR database under standard operating conditions and extended operating conditions. The experimental results show that SAR-HOG can reliably capture the structures of targets in SAR images, and SDDL can further capture subtle differences among the different classes. By virtue of the SAR-HOG feature and SDDLSR, the proposed method achieves the state-of-the-art performance on MSTAR database. Especially for the extended operating conditions (EOC) scenario “Training 17 —Testing 45 ”, the proposed method improves remarkably with respect to the previous works. View Full-Text
Keywords: synthetic aperture radar; target recognition; dictionary learning; sparse representation; histogram of oriented gradients; MSTAR synthetic aperture radar; target recognition; dictionary learning; sparse representation; histogram of oriented gradients; MSTAR
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Song, S.; Xu, B.; Yang, J. SAR Target Recognition via Supervised Discriminative Dictionary Learning and Sparse Representation of the SAR-HOG Feature. Remote Sens. 2016, 8, 683.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top