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Open AccessArticle

A Deep Convolutional Neural Network for Oil Spill Detection from Spaceborne SAR Images

by Kan Zeng 1,2 and Yixiao Wang 1,*
1
Ocean Remote Sensing Institute, Ocean University of China, Qingdao 266003, China
2
Laboratory for Regional Oceanography and Numerical Modeling, Pilot National Laboratory for Marine Science and Technology, Qingdao 266237, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(6), 1015; https://doi.org/10.3390/rs12061015
Received: 3 February 2020 / Revised: 19 March 2020 / Accepted: 19 March 2020 / Published: 22 March 2020
(This article belongs to the Special Issue Synthetic Aperture Radar Observations of Marine Coastal Environments)
Classification algorithms for automatically detecting sea surface oil spills from spaceborne Synthetic Aperture Radars (SARs) can usually be regarded as part of a three-step processing framework, which briefly includes image segmentation, feature extraction, and target classification. A Deep Convolutional Neural Network (DCNN), named the Oil Spill Convolutional Network (OSCNet), is proposed in this paper for SAR oil spill detection, which can do the latter two steps of the three-step processing framework. Based on VGG-16, the OSCNet is obtained by designing the architecture and adjusting hyperparameters with the data set of SAR dark patches. With the help of the big data set containing more than 20,000 SAR dark patches and data augmentation, the OSCNet can have as many as 12 weight layers. It is a relatively deep Deep Learning (DL) network for SAR oil spill detection. It is shown by the experiments based on the same data set that the classification performance of OSCNet has been significantly improved compared to that of traditional machine learning (ML). The accuracy, recall, and precision are improved from 92.50%, 81.40%, and 80.95% to 94.01%, 83.51%, and 85.70%, respectively. An important reason for this improvement is that the distinguishability of the features learned by OSCNet itself from the data set is significantly higher than that of the hand-crafted features needed by traditional ML algorithms. In addition, experiments show that data augmentation plays an important role in avoiding over-fitting and hence improves the classification performance. OSCNet has also been compared with other DL classifiers for SAR oil spill detection. Due to the huge differences in the data sets, only their similarities and differences are discussed at the principle level. View Full-Text
Keywords: Synthetic Aperture Radar (SAR); Deep Convolutional Neural Network (DCNN); oil spill detection; oil spills; lookalikes; dark patch Synthetic Aperture Radar (SAR); Deep Convolutional Neural Network (DCNN); oil spill detection; oil spills; lookalikes; dark patch
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MDPI and ACS Style

Zeng, K.; Wang, Y. A Deep Convolutional Neural Network for Oil Spill Detection from Spaceborne SAR Images. Remote Sens. 2020, 12, 1015.

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