A Deep Convolutional Neural Network for Oil Spill Detection from Spaceborne SAR Images
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 Convolu...
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doaj-6e0a259383524f7c9806c11961b603c92020-11-25T03:31:06ZengMDPI AGRemote Sensing2072-42922020-03-01126101510.3390/rs12061015rs12061015A Deep Convolutional Neural Network for Oil Spill Detection from Spaceborne SAR ImagesKan Zeng0Yixiao Wang1Ocean Remote Sensing Institute, Ocean University of China, Qingdao 266003, ChinaOcean Remote Sensing Institute, Ocean University of China, Qingdao 266003, ChinaClassification 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.https://www.mdpi.com/2072-4292/12/6/1015synthetic aperture radar (sar)deep convolutional neural network (dcnn)oil spill detectionoil spillslookalikesdark patch |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kan Zeng Yixiao Wang |
spellingShingle |
Kan Zeng Yixiao Wang A Deep Convolutional Neural Network for Oil Spill Detection from Spaceborne SAR Images Remote Sensing synthetic aperture radar (sar) deep convolutional neural network (dcnn) oil spill detection oil spills lookalikes dark patch |
author_facet |
Kan Zeng Yixiao Wang |
author_sort |
Kan Zeng |
title |
A Deep Convolutional Neural Network for Oil Spill Detection from Spaceborne SAR Images |
title_short |
A Deep Convolutional Neural Network for Oil Spill Detection from Spaceborne SAR Images |
title_full |
A Deep Convolutional Neural Network for Oil Spill Detection from Spaceborne SAR Images |
title_fullStr |
A Deep Convolutional Neural Network for Oil Spill Detection from Spaceborne SAR Images |
title_full_unstemmed |
A Deep Convolutional Neural Network for Oil Spill Detection from Spaceborne SAR Images |
title_sort |
deep convolutional neural network for oil spill detection from spaceborne sar images |
publisher |
MDPI AG |
series |
Remote Sensing |
issn |
2072-4292 |
publishDate |
2020-03-01 |
description |
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. |
topic |
synthetic aperture radar (sar) deep convolutional neural network (dcnn) oil spill detection oil spills lookalikes dark patch |
url |
https://www.mdpi.com/2072-4292/12/6/1015 |
work_keys_str_mv |
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