Oil Spill Contextual and Boundary-Supervised Detection Network Based on Marine SAR Images

Zhu, Qiqi; Zhang, Yanan; Li, Ziqi; Yan, Xiao-Rui; Guan, Qingfeng; Zhong, Yanfei; Zhang, Liangpei; Li, Deren

doi:10.1109/tgrs.2021.3115492

Cited by 43 publications

(15 citation statements)

References 46 publications

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“…In this paper, the Deep-SAR Oil Spill (SOS) dataset proposed by Zhu et al [ 21 ] was used to train and test the model. It consists of two parts: (1) 14 PALSAR images of the Gulf of Mexico region with pixel spacing of 12.5 m and HH polarization as the polarization mode; and (2) 7 Sentinel 1A images of the Persian Gulf region with a spatial resolution of 5 m × 20 m and VV polarization as the polarization mode.…”

Section: Methodsmentioning

confidence: 99%

“…The test set included a total of 776 images from PALSAR and 839 images from wave Sentinel-1. Some sample data from the Deep SAR Oil Spill (SOS) dataset are shown in Figure 1 [ 21 ].…”

Section: Methodsmentioning

confidence: 99%

“…Deep learning methods have the advantages of high learning ability, high generalization ability, and good adaptability [ 13 , 14 , 15 , 16 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ]. So far, there are many deep learning models used for image segmentation, such as the U-Net [ 13 ], which was first applied in the field of medical images [ 14 ], and the derived U-Net+ and U-Net++ models.…”

Section: Introductionmentioning

confidence: 99%

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A Dual Attention Encoding Network Using Gradient Profile Loss for Oil Spill Detection Based on SAR Images

Zhai

Hou

et al. 2022

Entropy

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Marine oil spills due to ship collisions or operational errors have caused tremendous damage to the marine environment. In order to better monitor the marine environment on a daily basis and reduce the damage and harm caused by oil pollution, we use marine image information acquired by synthetic aperture radar (SAR) and combine it with image segmentation techniques in deep learning to monitor oil spills. However, it is a significant challenge to accurately distinguish oil spill areas in original SAR images, which are characterized by high noise, blurred boundaries, and uneven intensity. Hence, we propose a dual attention encoding network (DAENet) using an encoder–decoder U-shaped architecture for identifying oil spill areas. In the encoding phase, we use the dual attention module to adaptively integrate local features with their global dependencies, thus improving the fusion feature maps of different scales. Moreover, a gradient profile (GP) loss function is used to improve the recognition accuracy of the oil spill areas’ boundary lines in the DAENet. We used the Deep-SAR oil spill (SOS) dataset with manual annotation for training, testing, and evaluation of the network, and we established a dataset containing original data from GaoFen-3 for network testing and performance evaluation. The results show that DAENet has the highest mIoU of 86.1% and the highest F1-score of 90.2% in the SOS dataset, and it has the highest mIoU of 92.3% and the highest F1-score of 95.1% in the GaoFen-3 dataset. The method proposed in this paper not only improves the detection and identification accuracy of the original SOS dataset, but also provides a more feasible and effective method for marine oil spill monitoring.

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Section: Methodsmentioning

confidence: 99%

“…The test set included a total of 776 images from PALSAR and 839 images from wave Sentinel-1. Some sample data from the Deep SAR Oil Spill (SOS) dataset are shown in Figure 1 [ 21 ].…”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Dual Attention Encoding Network Using Gradient Profile Loss for Oil Spill Detection Based on SAR Images

Zhai

Hou

et al. 2022

Entropy

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“…The most recent publications show an increasing focus on deep learning, employing deep neural networks, semantic segmentation, and convolutional neural networks to extract dark spots [53,54,66] and classify them as oil or lookalikes [67,68]. Some important efforts compared the performance of parametric and nonparametric algorithms to build oil detection systems.…”

Section: Machine Learning and Remote Sensing For Oil Slick Detectionmentioning

confidence: 99%

Improved Classification Models to Distinguish Natural from Anthropic Oil Slicks in the Gulf of Mexico: Seasonality and Radarsat-2 Beam Mode Effects under a Machine Learning Approach

et al. 2021

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Distinguishing between natural and anthropic oil slicks is a challenging task, especially in the Gulf of Mexico, where these events can be simultaneously observed and recognized as seeps or spills. In this study, a powerful data analysis provided by machine learning (ML) methods was employed to develop, test, and implement a classification model (CM) to distinguish an oil slick source (OSS) as natural or anthropic. A robust database containing 4916 validated oil samples, detected using synthetic aperture radar (SAR), was employed for this task. Six ML algorithms were evaluated, including artificial neural networks (ANN), random forest (RF), decision trees (DT), naive Bayes (NB), linear discriminant analysis (LDA), and logistic regression (LR). Using RF, the global CM achieved a maximum accuracy value of 73.15. An innovative approach evaluated how external factors, such as seasonality, satellite configurations, and the synergy between them, limit or improve OSS predictions. To accomplish this, specific classification models (SCMs) were derived from the global ones (CMs), tuning the best algorithms and parameters according to different scenarios. Median accuracies revealed winter and spring to be the best seasons and ScanSAR Narrow B (SCNB) as the best beam mode. The maximum median accuracy to distinguish seeps from spills was achieved in winter using SCNB (83.05). Among the tested algorithms, RF was the most robust, with a better performance in 81% of the investigated scenarios. The accuracy increment provided by the well-fitted models may minimize the confusion between seeps and spills. This represents a concrete contribution to reducing economic and geologic risks derived from exploration activities in offshore areas. Additionally, from an operational standpoint, specific models support specialists to select the best SAR products and seasons for new acquisitions, as well as to optimize performances according to the available data.

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“…Ma et al [17] used the amplitude and phase information of SAR data to extract polarimetric features and combined multi-layer features with a deep learning network model to achieve oil spill segmentation of SAR images, achieving excellent results in terms of accuracy and inference time. Zhu et al [39] proposed a contextual and boundarysupervised network (CBD-Net) for oil spill detection, which improved the extraction results of oil spill regions in SAR images with intensity inhomogeneity, high noise, and boundaryblurring by fusing multi-scale features, spatial and channel squeeze excitation block and joint loss functions. However, the localization approaches only capture the location of the oil spill and cannot obtain more information, such as the oil spill area.…”

mentioning

confidence: 99%

Multifeature Semantic Complementation Network for Marine Oil Spill Localization and Segmentation Based on SAR Images

Fan

Zhang

Wang

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Marine oil spill causes severe damage to the marine ecological environment. Synthetic aperture radar (SAR) is widely used in marine oil spill detection due to its all-day and all-weather advantages. However, long stripe shape oil spill areas make it challenging to extract the oil spills effectively. A multi-feature semantic complementation network (MFSCNet) is proposed for oil spill localization and segmentation of SAR images in one framework to address these problems. The long strip shape interference of oil spill is reduced by extracting intensity and damping ratio characters from non-polarimetric features and entropy, anisotropy, and mean scattering angle from polarization features to form a multi-feature SAR image. Then, the backbone feature network and feature fusion module are used for feature extraction. The decoupled head and the proposed oil spill semantic segmentation head are used for localization and semantic segmentation tasks, respectively. Also, the semantic complementation module is used in the training phase. It combines the results of localization and semantic segmentation to obtain complementation boxes for interactive iterative updating of the model parameters to enhance the detection accuracy of localization boxes. The effectiveness of the proposed model is demonstrated based on a lot of Sentinel-1 oil spill data compared with other state-of-the-art methods. The code of this work will be available at https://github.com/fjc1575/Marine-Oil-Spill/tree/ main/MFSCNet for the sake of reproducibility.

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Oil Spill Contextual and Boundary-Supervised Detection Network Based on Marine SAR Images

Cited by 43 publications

References 46 publications

A Dual Attention Encoding Network Using Gradient Profile Loss for Oil Spill Detection Based on SAR Images

A Dual Attention Encoding Network Using Gradient Profile Loss for Oil Spill Detection Based on SAR Images

Improved Classification Models to Distinguish Natural from Anthropic Oil Slicks in the Gulf of Mexico: Seasonality and Radarsat-2 Beam Mode Effects under a Machine Learning Approach

Multifeature Semantic Complementation Network for Marine Oil Spill Localization and Segmentation Based on SAR Images

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