A New Endmember Preprocessing Method for the Hyperspectral Unmixing of Imagery Containing Marine Oil Spills

Cui, Can; Liu, Ying; Liu, Bing-Xin; Li, Guannan

doi:10.3390/ijgi6090286

Cited by 10 publications

(4 citation statements)

References 25 publications

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“…Among these, hyperspectral remote sensing, due to its higher spectral and spatial resolution, enables the extraction of subtle features between oil slicks and background seawater, as well as distinguishing different oil types. Consequently, it has found extensive applications in monitoring the location and distribution of oil spills [39][40][41][42][43], identifying oil types [44][45][46][47][48], and quantifying oil slick thickness [49][50][51][52].…”

Section: Introductionmentioning

confidence: 99%

Hyperspectral Marine Oil Spill Monitoring Using a Dual-Branch Spatial–Spectral Fusion Model

Yang,

Wang,

et al. 2023

Remote Sensing

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Marine oil spills pose a crucial concern in the monitoring of marine environments, and optical remote sensing serves as a vital means for marine oil spill detection. However, optical remote sensing imagery is susceptible to interference from sunglints and shadows, leading to diminished spectral differences between oil films and seawater. This makes it challenging to accurately extract the boundaries of oil–water interfaces. To address these aforementioned issues, this paper proposes a model based on the graph convolutional architecture and spatial–spectral information fusion for the oil spill detection of real oil spill incidents. The model is experimentally evaluated using both spaceborne and airborne hyperspectral oil spill images. Research findings demonstrate the superior oil spill detection accuracy of the developed model when compared to Graph Convolutional Network (GCN) and CNN-Enhanced Graph Convolutional Network (CEGCN), across two hyperspectral datasets collected from the Bohai Sea. Moreover, the performance of the developed model in oil spill detection remains optimal, even with only 1% of the training samples. Similar conclusions are drawn from the oil spill hyperspectral data collected from the Yellow Sea. These results validate the efficacy and robustness of the proposed model for marine oil spill detection.

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Section: Introductionmentioning

confidence: 99%

Hyperspectral Marine Oil Spill Monitoring Using a Dual-Branch Spatial–Spectral Fusion Model

Yang,

Wang,

et al. 2023

Remote Sensing

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“…In recent years, sudden oil spill accidents have become more frequent with increasing maritime traffic. These accidents include oil pipeline ruptures, oil and gas leakages, vessel collisions, illegal dumping, and blowouts, causing serious damage to the marine environment and ecological resources [1][2][3]. To manage oil spill detection and post-disaster cleanup, planners require instantaneous information regarding the location, type, distribution, and thickness of an oil slick [4,5].…”

Section: Introductionmentioning

confidence: 99%

A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill

Liu

et al. 2019

IJGI

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Spectral characteristics play an important role in the classification of oil film, but the presence of too many bands can lead to information redundancy and reduced classification accuracy. In this study, a classification model that combines spectral indices-based band selection (SIs) and one-dimensional convolutional neural networks was proposed to realize automatic oil films classification using hyperspectral remote sensing images. Additionally, for comparison, the minimum Redundancy Maximum Relevance (mRMR) was tested for reducing the number of bands. The support vector machine (SVM), random forest (RF), and Hu’s convolutional neural networks (CNN) were trained and tested. The results show that the accuracy of classifications through the one dimensional convolutional neural network (1D CNN) models surpassed the accuracy of other machine learning algorithms such as SVM and RF. The model of SIs+1D CNN could produce a relatively higher accuracy oil film distribution map within less time than other models.

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“…Oil pollution has received extensive public attention on account of the resulting ecological destruction, including oil floating on the sea surface, residual oil deposited on coastlines, sunken and submerged oil, oiled marine animals, and areas of oil-polluted sea ice [1][2][3][4]. Alves and Zodiatis have conducted some of the most complete studies of oil spill response in the form of an assessment of the impact of oil spills on human activity and shorelines; offshore susceptibility; and clean-up, and mitigation efforts in the Mediterranean Basin and the Baltic Sea based on some worthy projects on oil spill response [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Analysis of Scattering Properties of Continuous Slow-Release Slicks on the Sea Surface Based on Polarimetric Synthetic Aperture Radar

Liu

et al. 2018

IJGI

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This study employs polarimetric synthetic aperture radar (Pol-SAR) to examine the scattering properties of continuous slow-release oil slicks on the sea surface. The objective is to extract and analyze the general polarization scattering properties of continuous slow-release slicks, i.e., those slicks that consist of substances released at a fairly slow and relatively constant rate, and to determine the influence of the slick formation process on these properties. Using multi-polarization feature parameters derived from the averaged coherency matrix, we find that the scattering mechanisms related to the continuous slow-release slicks differ from those of anthropogenic slicks, possibly as a result of the multiple scattering mechanisms that occur between the interfaces formed by the thick slick layer. Combinations of entropy (H) and modified anisotropy (A 12 ) are relatively robust parameters for identifying continuous slow-release slicks under different sea conditions, and may serve as a reference parameter for slick detection.

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A New Endmember Preprocessing Method for the Hyperspectral Unmixing of Imagery Containing Marine Oil Spills

Cited by 10 publications

References 25 publications

Hyperspectral Marine Oil Spill Monitoring Using a Dual-Branch Spatial–Spectral Fusion Model

Hyperspectral Marine Oil Spill Monitoring Using a Dual-Branch Spatial–Spectral Fusion Model

A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill

Analysis of Scattering Properties of Continuous Slow-Release Slicks on the Sea Surface Based on Polarimetric Synthetic Aperture Radar

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