Application of Deep Networks to Oil Spill Detection Using Polarimetric Synthetic Aperture Radar Images

Chen, Guandong; Li, Yu; Sun, Guangmin; Zhang, Yuanzhi

doi:10.3390/app7100968

Cited by 67 publications

(40 citation statements)

References 33 publications

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“…In this paper, a kind of deep neural network-Stacked-autoencoder (SAE) is considered as the classification algorithm, since it has been proved in previous studies to have the best performance on oil spills detection based on polarimetric SAR data [21]. Deep neural networks have very good capability in fitting complex functions.…”

Section: Deep Learning Classification Algorithmmentioning

confidence: 99%

Marine Oil Spill Detection Based on the Comprehensive Use of Polarimetric SAR Data

Zhang

Yuan

et al. 2018

Sustainability

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As a major marine pollution source, oil spills largely threaten the sustainability of the coastal environment. Polarimetric synthetic aperture radar remote sensing has become a promising approach for marine oil spill detection since it could effectively separate crude oil and biogenic look-alikes. However, on the sea surface, the signal to noise ratio of Synthetic Aperture Radar (SAR) backscatter is usually low, especially for cross-polarized channels. In practice, it is necessary to combine the refined detail of slick-sea boundary derived from the co-polarized channel and the highly accurate crude slick/look-alike classification result obtained based on the polarimetric information. In this paper, the architecture for oil spill detection based on polarimetric SAR is proposed and analyzed. The performance of different polarimetric SAR filters for oil spill classification are compared. Polarimetric SAR features are extracted and taken as the input of Staked Auto Encoder (SAE) to achieve high accurate classification between crude oil, biogenic slicks, and clean sea surface. A post-processing method is proposed to combine the classification result derived from SAE and the refined boundary derived from VV channel power image based on special density thresholding (SDT). Experiments were conducted on spaceborne fully polarimetric SAR images where both crude oil and biogenic slicks were presented on the sea surface.

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Section: Deep Learning Classification Algorithmmentioning

confidence: 99%

Marine Oil Spill Detection Based on the Comprehensive Use of Polarimetric SAR Data

Zhang

Yuan

et al. 2018

Sustainability

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“…Chen, G., Li, Y., Sun, G., Zhang, Y. [6] used polarimetric synthetic aperture radar images to detect oil spills. Hwang, J., Chae, S., Kim, D., Jung, H. [7] used X-band Kompsat-5 images to detect ships.…”

Section: Applications Of Artificial Neural Network In Geoinformaticsmentioning

confidence: 99%

Editorial for Special Issue: “Application of Artificial Neural Networks in Geoinformatics”

Lee

2018

Applied Sciences

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“…With the rise of machine learning algorithms in recent years, neural networks have also been applied into oil spill detection. Yu Li et al performed several comparative experiments between different machine learning classifiers based on multi polarized parameters [17], and the differences between fully and compact polarimetric SAR images [18] were explored.…”

Section: Introductionmentioning

confidence: 99%

Oil Spill Detection in Quad-Polarimetric SAR Images Using an Advanced Convolutional Neural Network Based on SuperPixel Model

et al. 2020

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Oil spill detection plays an important role in marine environment protection. Quad-polarimetric Synthetic Aperture Radar (SAR) has been proved to have great potential for this task, and different SAR polarimetric features have the advantages to recognize oil spill areas from other look-alikes. In this paper we proposed an oil spill detection method based on convolutional neural network (CNN) and Simple Linear Iterative Clustering (SLIC) superpixel. Experiments were conducted on three Single Look Complex (SLC) quad-polarimetric SAR images obtained by Radarsat-2 and Spaceborne Imaging Radar-C/X-Band Synthetic Aperture Radar (SIR-C/X-SAR). Several groups of polarized parameters, including H/A/Alpha decomposition, Single-Bounce Eigenvalue Relative Difference (SERD), correlation coefficients, conformity coefficients, Freeman 3-component decomposition, Yamaguchi 4-component decomposition were extracted as feature sets. Among all considered polarimetric features, Yamaguchi parameters achieved the highest performance with total Mean Intersection over Union (MIoU) of 90.5%. It is proved that the SLIC superpixel method significantly improved the oil spill classification accuracy on all the polarimetric feature sets. The classification accuracy of all kinds of targets types were improved, and the largest increase on mean MIoU of all features sets was on emulsions by 21.9%.

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Application of Deep Networks to Oil Spill Detection Using Polarimetric Synthetic Aperture Radar Images

Cited by 67 publications

References 33 publications

Marine Oil Spill Detection Based on the Comprehensive Use of Polarimetric SAR Data

Marine Oil Spill Detection Based on the Comprehensive Use of Polarimetric SAR Data

Editorial for Special Issue: “Application of Artificial Neural Networks in Geoinformatics”

Oil Spill Detection in Quad-Polarimetric SAR Images Using an Advanced Convolutional Neural Network Based on SuperPixel Model

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