Next Article in Journal
Activity Recognition Invariant to Sensor Orientation with Wearable Motion Sensors
Next Article in Special Issue
Analytical Modeling Tool for Design of Hydrocarbon Sensitive Optical Fibers
Previous Article in Journal
A Comprehensive System for Monitoring Urban Accessibility in Smart Cities
Previous Article in Special Issue
Hyperspectral and Radar Airborne Imagery over Controlled Release of Oil at Sea
Article Menu
Issue 8 (August) cover image

Export Article

Open AccessArticle
Sensors 2017, 17(8), 1837; https://doi.org/10.3390/s17081837

Discrimination of Oil Slicks and Lookalikes in Polarimetric SAR Images Using CNN

Information Science and Technology College, Dalian Maritime University, Dalian 116026, China
*
Author to whom correspondence should be addressed.
Received: 3 July 2017 / Revised: 2 August 2017 / Accepted: 7 August 2017 / Published: 9 August 2017
(This article belongs to the Special Issue Sensors for Oil Applications)
View Full-Text   |   Download PDF [9238 KB, uploaded 9 August 2017]   |  

Abstract

Oil slicks and lookalikes (e.g., plant oil and oil emulsion) all appear as dark areas in polarimetric Synthetic Aperture Radar (SAR) images and are highly heterogeneous, so it is very difficult to use a single feature that can allow classification of dark objects in polarimetric SAR images as oil slicks or lookalikes. We established multi-feature fusion to support the discrimination of oil slicks and lookalikes. In the paper, simple discrimination analysis is used to rationalize a preferred features subset. The features analyzed include entropy, alpha, and Single-bounce Eigenvalue Relative Difference (SERD) in the C-band polarimetric mode. We also propose a novel SAR image discrimination method for oil slicks and lookalikes based on Convolutional Neural Network (CNN). The regions of interest are selected as the training and testing samples for CNN on the three kinds of polarimetric feature images. The proposed method is applied to a training data set of 5400 samples, including 1800 crude oil, 1800 plant oil, and 1800 oil emulsion samples. In the end, the effectiveness of the method is demonstrated through the analysis of some experimental results. The classification accuracy obtained using 900 samples of test data is 91.33%. It is here observed that the proposed method not only can accurately identify the dark spots on SAR images but also verify the ability of the proposed algorithm to classify unstructured features. View Full-Text
Keywords: Synthetic Aperture Radar (SAR); pattern recognition; oil slicks; lookalikes; feature fusion; Convolutional Neural Network (CNN) Synthetic Aperture Radar (SAR); pattern recognition; oil slicks; lookalikes; feature fusion; Convolutional Neural Network (CNN)
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

Guo, H.; Wu, D.; An, J. Discrimination of Oil Slicks and Lookalikes in Polarimetric SAR Images Using CNN. Sensors 2017, 17, 1837.

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