A novel nonlinear hyperspectral unmixing approach for images of oil spills at sea

Li, Ying; Lu, Huimin; Zhang, Zhenduo; Liu, Peng

doi:10.1080/01431161.2020.1723179

Cited by 13 publications

(4 citation statements)

References 45 publications

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“…The high spectral resolution is vital considering the spectral similarity between diverse oil types, such as crude, diesel, lubricant, and kerosene, and subtle differences can be distinguished using hyperspectral RS technology [81]. Several studies on oil spills use hyperspectral systems, including airborne visible/infrared imaging spectrometry [22,23,[82][83][84][85][86][87], Hyperion [81,88], and analytical spectral devices [89]. However, the requirement of advanced processing and analysis in hyperspectral images is a setback for real-time monitoring [15].…”

Section: Optical Datamentioning

confidence: 99%

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

et al. 2020

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Remote sensing technologies and machine learning (ML) algorithms play an increasingly important role in accurate detection and monitoring of oil spill slicks, assisting scientists in forecasting their trajectories, developing clean-up plans, taking timely and urgent actions, and applying effective treatments to contain and alleviate adverse effects. Review and analysis of different sources of remotely sensed data and various components of ML classification systems for oil spill detection and monitoring are presented in this study. More than 100 publications in the field of oil spill remote sensing, published in the past 10 years, are reviewed in this paper. The first part of this review discusses the strengths and weaknesses of different sources of remotely sensed data used for oil spill detection. Necessary preprocessing and preparation of data for developing classification models are then highlighted. Feature extraction, feature selection, and widely used handcrafted features for oil spill detection are subsequently introduced and analyzed. The second part of this review explains the use and capabilities of different classical and developed state-of-the-art ML techniques for oil spill detection. Finally, an in-depth discussion on limitations, open challenges, considerations of oil spill classification systems using remote sensing, and state-of-the-art ML algorithms are highlighted along with conclusions and insights into future directions.

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Section: Optical Datamentioning

confidence: 99%

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

et al. 2020

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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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“…Oil spill prevention and response operations cover multiple stages, including spill occurrence, spill monitoring, decision-making and emergency response (Ye et al 2020). Among them, rapid and effective oil spill monitoring is an auxiliary means to make decision and prevent the further spread of pollution (Li et al 2020). Oil spill detection ability based on remote sensing images is important in marine environmental monitoring.…”

Section: Introductionmentioning

confidence: 99%

Oil spill discrimination of multi-time-domain shipborne radar images using active contour model

Pan

et al. 2021

Geosci. Lett.

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Accidental oil spills cause serious pollution to the ocean and are difficult to control in short time. It is an important guarantee for emergency disposal to effectively monitor oil spills. Remote sensing is the main means to monitor oil spills. High false alarm rate has been an important bottleneck of this technology. In this paper, a multi-time-domain shipborne radar images discrimination mechanism was proposed. Based on the improved Sobel operator, Otsu and linear interpolation, the co-frequency interference noises were detected and suppressed. Gray intensity correction model (GICM) and dual-threshold method were used to eliminate highlighted continuous pixels. Oil films were extracted by using an active contour model (ACM). Finally, a multi-time-domain discrimination mechanism based on variation range tolerance of identified oil films centroids was designed to reduce the false alarm rate. It can provide technical support for decision-making and emergency response.

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A novel nonlinear hyperspectral unmixing approach for images of oil spills at sea

Cited by 13 publications

References 45 publications

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review

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

Oil spill discrimination of multi-time-domain shipborne radar images using active contour model

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