Next Article in Journal
Investigation on Inter-Limb Coordination and Motion Stability, Intensity and Complexity of Trunk and Limbs during Hands-Knees Crawling in Human Adults
Previous Article in Journal
Intelligent Diagnosis Method for Rotating Machinery Using Dictionary Learning and Singular Value Decomposition
Article Menu
Issue 4 (April) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(4), 686; doi:10.3390/s17040686

A Correlation-Based Joint CFAR Detector Using Adaptively-Truncated Statistics in SAR Imagery

1
School of Computer and Information, Hefei University of Technology, Tunxi Road, Hefei 230009, China
2
College of Electronic and Information Engineering, Nanjing University of Aeronautics and Astronautics, Yudao Avenue, Nanjing 210016, China
*
Author to whom correspondence should be addressed.
Academic Editor: Jonathan Li
Received: 9 December 2016 / Revised: 13 March 2017 / Accepted: 21 March 2017 / Published: 27 March 2017
(This article belongs to the Section Remote Sensors)
View Full-Text   |   Download PDF [7747 KB, uploaded 27 March 2017]   |  

Abstract

Traditional constant false alarm rate (CFAR) detectors only use the contrast information between ship targets and clutter, and they suffer probability of detection (PD) degradation in multiple target situations. This paper proposes a correlation-based joint CFAR detector using adaptively-truncated statistics (hereafter called TS-2DLNCFAR) in SAR images. The proposed joint CFAR detector exploits the gray intensity correlation characteristics by building a two-dimensional (2D) joint log-normal model as the joint distribution (JPDF) of the clutter, so joint CFAR detection is realized. Inspired by the CFAR detection methodology, we design an adaptive threshold-based clutter truncation method to eliminate the high-intensity outliers, such as interfering ship targets, side-lobes, and ghosts in the background window, whereas the real clutter samples are preserved to the largest degree. A 2D joint log-normal model is accurately built using the adaptively-truncated clutter through simple parameter estimation, so the joint CFAR detection performance is greatly improved. Compared with traditional CFAR detectors, the proposed TS-2DLNCFAR detector achieves a high PD and a low false alarm rate (FAR) in multiple target situations. The superiority of the proposed TS-2DLNCFAR detector is validated on the multi-look Envisat-ASAR and TerraSAR-X data. View Full-Text
Keywords: SAR; ship detection; correlation-based joint CFAR; 2D joint log-normal distribution; adaptively truncated clutter statistics SAR; ship detection; correlation-based joint CFAR; 2D joint log-normal distribution; adaptively truncated clutter statistics
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

Ai, J.; Yang, X.; Zhou, F.; Dong, Z.; Jia, L.; Yan, H. A Correlation-Based Joint CFAR Detector Using Adaptively-Truncated Statistics in SAR Imagery. Sensors 2017, 17, 686.

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