Next Article in Journal
Earable POCER: Development of a Point-of-Care Ear Sensor for Respiratory Rate Measurement
Next Article in Special Issue
A Lightweight Convolutional Neural Network Based on Visual Attention for SAR Image Target Classification
Previous Article in Journal
Improving GNSS Ambiguity Acceptance Test Performance with the Generalized Difference Test Approach
Previous Article in Special Issue
Terahertz Image Detection with the Improved Faster Region-Based Convolutional Neural Network
Article Menu
Issue 9 (September) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(9), 3019; https://doi.org/10.3390/s18093019

Target Recognition of SAR Images via Matching Attributed Scattering Centers with Binary Target Region

1
Hainan Key Laboratory of Earth Observation, Sanya 572029, China
2
Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
3
School of Public Administration and Mass Media, Beijing Information Science and Technology University, Beijing 100093, China
*
Author to whom correspondence should be addressed.
Received: 25 July 2018 / Revised: 5 September 2018 / Accepted: 5 September 2018 / Published: 10 September 2018
(This article belongs to the Special Issue Automatic Target Recognition of High Resolution SAR/ISAR Images)
Full-Text   |   PDF [5624 KB, uploaded 10 September 2018]   |  

Abstract

A target recognition method of synthetic aperture radar (SAR) images is proposed via matching attributed scattering centers (ASCs) to binary target regions. The ASCs extracted from the test image are predicted as binary regions. In detail, each ASC is first transformed to the image domain based on the ASC model. Afterwards, the resulting image is converted to a binary region segmented by a global threshold. All the predicted binary regions of individual ASCs from the test sample are mapped to the binary target regions of the corresponding templates. Then, the matched regions are evaluated by three scores which are combined as a similarity measure via the score-level fusion. In the classification stage, the target label of the test sample is determined according to the fused similarities. The proposed region matching method avoids the conventional ASC matching problem, which involves the assignment of ASC sets. In addition, the predicted regions are more robust than the point features. The Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset is used for performance evaluation in the experiments. According to the experimental results, the method in this study outperforms some traditional methods reported in the literature under several different operating conditions. Under the standard operating condition (SOC), the proposed method achieves very good performance, with an average recognition rate of 98.34%, which is higher than the traditional methods. Moreover, the robustness of the proposed method is also superior to the traditional methods under different extended operating conditions (EOCs), including configuration variants, large depression angle variation, noise contamination, and partial occlusion. View Full-Text
Keywords: synthetic aperture radar (SAR); target recognition; attributed scattering center (ASC); region matching; score fusion synthetic aperture radar (SAR); target recognition; attributed scattering center (ASC); region matching; score fusion
Figures

Graphical abstract

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

Share & Cite This Article

MDPI and ACS Style

Tan, J.; Fan, X.; Wang, S.; Ren, Y. Target Recognition of SAR Images via Matching Attributed Scattering Centers with Binary Target Region. Sensors 2018, 18, 3019.

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