Next Article in Journal
Concept and Evaluation of a New Piezoelectric Transducer for an Implantable Middle Ear Hearing Device
Previous Article in Journal
Design and Electro-Thermo-Mechanical Behavior Analysis of Au/Si3N4 Bimorph Microcantilevers for Static Mode Sensing
Article Menu
Issue 11 (November) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(11), 2506; doi:10.3390/s17112506

Two-Stage Multi-Task Representation Learning for Synthetic Aperture Radar (SAR) Target Images Classification

1
College of Communication Engineering, Chongqing University, Chongqing 400044, China
2
Key Laboratory of Aerocraft Tracking Telementering & Command and Communication, Chongqing University, Chongqing 400044, China
*
Author to whom correspondence should be addressed.
Received: 24 August 2017 / Revised: 4 October 2017 / Accepted: 28 October 2017 / Published: 1 November 2017
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [5683 KB, uploaded 1 November 2017]   |  

Abstract

In this paper, we propose a two-stage multi-task learning representation method for the classification of synthetic aperture radar (SAR) target images. The first stage of the proposed approach uses multi-features joint sparse representation learning, modeled as a 2 , 1 -norm regularized multi-task sparse learning problem, to find an effective subset of training samples. Then, a new dictionary is constructed based on the training subset. The second stage of the method is to perform target images classification based on the new dictionary, utilizing multi-task collaborative representation. The proposed algorithm not only exploits the discrimination ability of multiple features but also greatly reduces the interference of atoms that are irrelevant to the test sample, thus effectively improving classification performance. Conducted with the Moving and Stationary Target Acquisition and Recognition (MSTAR) public SAR database, experimental results show that the proposed approach is effective and superior to many state-of-the-art methods. View Full-Text
Keywords: SAR; images classification; multitask learning; sparse representation; collaborative representation SAR; images classification; multitask learning; sparse representation; collaborative representation
Figures

Figure 1

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

Zhang, X.; Wang, Y.; Tan, Z.; Li, D.; Liu, S.; Wang, T.; Li, Y. Two-Stage Multi-Task Representation Learning for Synthetic Aperture Radar (SAR) Target Images Classification. Sensors 2017, 17, 2506.

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]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top