Next Article in Journal
A Coalitional Formation Game for Physical Layer Security of Cooperative Compressive Sensing Multi-Relay Networks
Previous Article in Journal
A Capillary Computing Architecture for Dynamic Internet of Things: Orchestration of Microservices from Edge Devices to Fog and Cloud Providers
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(9), 2940;

Adaptive Local Aspect Dictionary Pair Learning for Synthetic Aperture Radar Target Image Classification

College of Communication Engineering, Chongqing University, Chongqing 400044, China
Chongqing Key Lab of Mobile Communications Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Spacecraft General Design Department, China Academy of Space Technology, Beijing 100094, China
Author to whom correspondence should be addressed.
Received: 3 June 2018 / Revised: 28 August 2018 / Accepted: 31 August 2018 / Published: 4 September 2018
(This article belongs to the Section Remote Sensors)
Full-Text   |   PDF [7481 KB, uploaded 4 September 2018]   |  


In this paper, a new target classification algorithm based on adaptive local aspect dictionary pair learning for synthetic aperture radar (SAR) images is developed. To that end, first, the aspect sector of one testing sample is determined adaptively by a regularized non-negative sparse learning method. Second, a synthesis dictionary and an analysis dictionary are jointly learned from the corresponding training subset located in the aspect sector. By doing so, the local aspect dictionary pair is obtained. Finally, the class label of the testing sample is inferred by a use of the minimum reconstruction residual under the representation with the local aspect dictionary pair. Using the local aspect sector training subset rather than the global aspect training set reduces the interference of a large amount of unrelated training samples, which leads to a more discriminative local aspect dictionary pair for target classification. The experiments are conducted with the Moving and Stationary Target Acquisition and Recognition (MSTAR) database, and the results demonstrate that the proposed approach is effective and superior to the state-of-the-art methods. View Full-Text
Keywords: SAR; images classification; dictionary learning; representation learning; aspect SAR; images classification; dictionary learning; representation learning; aspect

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).

Share & Cite This Article

MDPI and ACS Style

Zhang, X.; Tan, Z.; Liu, G.; Liu, H.; Wang, Y.; Liu, S.; Li, Y.; Xu, H.; Xia, J. Adaptive Local Aspect Dictionary Pair Learning for Synthetic Aperture Radar Target Image Classification. Sensors 2018, 18, 2940.

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



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