Next Article in Journal
Human Part Segmentation in Depth Images with Annotated Part Positions
Next Article in Special Issue
Land Cover Classification with GF-3 Polarimetric Synthetic Aperture Radar Data by Random Forest Classifier and Fast Super-Pixel Segmentation
Previous Article in Journal
An IBeacon-Based Location System for Smart Home Control
Previous Article in Special Issue
Adaptive Unscented Kalman Filter Phase Unwrapping Method and Its Application on Gaofen-3 Interferometric SAR Data
Article Menu
Issue 6 (June) cover image

Export Article

Open AccessArticle
Sensors 2018, 18(6), 1898; https://doi.org/10.3390/s18061898

Coastline Detection with Gaofen-3 SAR Images Using an Improved FCM Method

1
,
2,3,* , 1,4
,
1,4
and
1
1
School of Geosciences and Info-physics, Central South University, Changsha 410083, China
2
College of Resources and Environmental Science, Hunan Normal University, Changsha 410081, China
3
Key Laboratory of Geospatial Big Data Mining and Application, Hunan Normal University, Changsha 410081, China
4
Key Laboratory of Metallogenic Prediction of Nonferrous Metals and Geological Environment Monitoring (Central South University), Ministry of Education, Changsha 410083, China
*
Author to whom correspondence should be addressed.
Received: 28 March 2018 / Revised: 23 May 2018 / Accepted: 28 May 2018 / Published: 11 June 2018
(This article belongs to the Special Issue First Experiences with Chinese Gaofen-3 SAR Sensor)
View Full-Text   |   Download PDF [6702 KB, uploaded 11 June 2018]   |  

Abstract

The coastline detection is one of the main applications of the Gaofen-3 satellite in the ocean field. However, the capability of Gaofen-3 SAR image in coastline detection has not yet been validated. In this paper, two Gaofen-3 SAR images, acquired in 2016, were used to extract the coastlines of the regions of Bohai and Taihu in China, respectively. The classical Fuzzy C-means (FCM) method was used in the coastline detection, but had been improved by combining the Wavelet decomposition algorithm to better suppress the inherent speckle noises of SAR image. Coastline detection results obtained from two Sentinel-1 SAR images acquired on the same regions were compared with those of the Gaofen-3 images. By using the manually delineated coastlines as the standards in the qualitative evaluations, improvements of about 12.0%, 8.3%, 23.8%, and 9.4% can be achieved by the improved FCM method with respect to the indicators of mean, RMSE, PGSD, and P90%, respectively; demonstrating that the Gaofen-3 data is superior to the Sentinel-1 data in the detection of coastline. View Full-Text
Keywords: Gaofen-3; SAR; coastline detection; Bohai; Taihu; FCM; wavelet decomposition Gaofen-3; SAR; coastline detection; Bohai; Taihu; FCM; wavelet decomposition
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

An, M.; Sun, Q.; Hu, J.; Tang, Y.; Zhu, Z. Coastline Detection with Gaofen-3 SAR Images Using an Improved FCM Method. Sensors 2018, 18, 1898.

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