Next Article in Journal
Optimizing the MAC Protocol in Localization Systems Based on IEEE 802.15.4 Networks
Next Article in Special Issue
Preliminary Assessment of Wind and Wave Retrieval from Chinese Gaofen-3 SAR Imagery
Previous Article in Journal
Symbiotic Navigation in Multi-Robot Systems with Remote Obstacle Knowledge Sharing
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(7), 1578; https://doi.org/10.3390/s17071578

Fast Vessel Detection in Gaofen-3 SAR Images with Ultrafine Strip-Map Mode

1,2,* , 1,2
,
1,2
and
1,2
1
Key Laboratory of Technology in Geo-Spatial Information Processing and Application System, Chinese Academy of Sciences, Beijing 100190, China
2
Institute of Electronics, Chinese Academy of Sciences, Beijing 100190, China
*
Author to whom correspondence should be addressed.
Received: 1 May 2017 / Revised: 27 June 2017 / Accepted: 28 June 2017 / Published: 5 July 2017
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
Full-Text   |   PDF [4064 KB, uploaded 5 July 2017]   |  

Abstract

This study aims to detect vessels with lengths ranging from about 70 to 300 m, in Gaofen-3 (GF-3) SAR images with ultrafine strip-map (UFS) mode as fast as possible. Based on the analysis of the characteristics of vessels in GF-3 SAR imagery, an effective vessel detection method is proposed in this paper. Firstly, the iterative constant false alarm rate (CFAR) method is employed to detect the potential ship pixels. Secondly, the mean-shift operation is applied on each potential ship pixel to identify the candidate target region. During the mean-shift process, we maintain a selection matrix recording which pixels can be taken, and these pixels are called as the valid points of the candidate target. The l 1 norm regression is used to extract the principal axis and detect the valid points. Finally, two kinds of false alarms, the bright line and the azimuth ambiguity, are removed by comparing the valid area of the candidate target with a pre-defined value and computing the displacement between the true target and the corresponding replicas respectively. Experimental results on three GF-3 SAR images with UFS mode demonstrate the effectiveness and efficiency of the proposed method. View Full-Text
Keywords: vessel detection; iterative CFAR approach; mean-shift based coarse detection; false alarms elimination; Gaofen-3 SAR images; ultrafine strip-map mode vessel detection; iterative CFAR approach; mean-shift based coarse detection; false alarms elimination; Gaofen-3 SAR images; ultrafine strip-map mode
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

Share & Cite This Article

MDPI and ACS Style

Pan, Z.; Liu, L.; Qiu, X.; Lei, B. Fast Vessel Detection in Gaofen-3 SAR Images with Ultrafine Strip-Map Mode. Sensors 2017, 17, 1578.

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