Study on the hyperspectral polarized reflection characteristics of oil slicks on sea surfaces

Sun, Zhongqiu; Zhao, Yunsheng; Yan, Guo-Qian; Li, Shaoping

doi:10.1007/s11434-010-4112-5

Cited by 23 publications

(16 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The spectra of very thick oil spills exhibited poor reflection when compared with the other types of oil spills; the thin oil spills showed high reflectance. This study suggests that the spectral bands 3 and 4 of MSI are best to use for image processing and discriminate oil spills [ 19 , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] ]. The spectral bands acquired in these wavelengths by other satellites can also discriminate oil spills.…”

Section: Details Of Methodsmentioning

confidence: 99%

Sentinel-2 image transformation methods for mapping oil spill – A case study with Wakashio oil spill in the Indian Ocean, off Mauritius

et al. 2021

View full text Add to dashboard Cite

Although several indices have been constructed and available at the Index database (IDB) for Sentinel-2 satellite to map and study several earth resources, no indices have been developed to map oil spill. We constructed band ratios (5 + 6)/7, (3 + 4)/2, (11+12)/8 and 3/2, (3 + 4)/2, (6 + 7)/5 using the high-resolution MSI (multi-spectral instrument) visible-near infrared-shortwave infrared spectral bands of Sentinel-2 by summing-up the bands representing the shoulders of absorption features as numerator and the band located nearest to the absorption feature as denominator to discriminate oil spill, and demonstrate the potential of this method to map the Wakashio oil spill which occurred in the Indian Ocean, off Mauritius. The resulted images discriminated the oil spill well. We also decorrelated the spectral bands 4, 3 and 2 by studying the spectral band absorptions and discriminated the spill as very thick, thick and thin. The results of decorrelation stretch method exhibited the distribution of types of oil spill in a different tone, distinctly. Both the image transformation methods (band ratios and decorrelation stretch methods) showed their capability to map oil spills, and these methods are recommended to use for similar spectral bands of other sensors to map oil spills. This study demonstrated the application of band ratios and decorrelation stretch methods to map oil spill. The methods discriminated the oil spill off Mauritius, and showed spill thicknesses from the Sentinel-2 data. The new methods are recommended to use for the spectral bands of other sensors to map oil spill.

show abstract

Section: Details Of Methodsmentioning

confidence: 99%

Sentinel-2 image transformation methods for mapping oil spill – A case study with Wakashio oil spill in the Indian Ocean, off Mauritius

et al. 2021

View full text Add to dashboard Cite

show abstract

“…A potential method of improving the classification model and applying it with coarser hyperspectral data would be a remote sensing data fusion approach that combines reflectance spectra with polarization imaging. Since the polarization characteristics of oil surface are able to reveal more information about the roughness and texture than using reflectance spectra along [41], they have been applied to the field of oil spill detection [42]- [44]. Therefore, we envision a fusion approach which may be able to distinguish between different types of oil that have similar reflectance spectra, and achieve accurate classification using hyperspectral data with coarser bands.…”

Section: Conclusion and Future Studymentioning

confidence: 99%

Identifying Oil Spill Types Based on Remotely Sensed Reflectance Spectra and Multiple Machine Learning Algorithms

Liu¹,

Yu²,

Xie³

et al. 2021

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

View full text Add to dashboard Cite

An accurate identification of oil spill types is the basis of determining the source of leakage, evaluating the potential damage, and deciding a plan of responses for an oil spill event. Despite sufficient studies that interpreted and analyzed hyperspectral data of oil spills, these studies that identify or classify oil spill types is rather limited. Aiming at identifying different types of oil spills, this study analyses the reflectance spectra obtained from high-resolution hyperspectral sensors using multiple machine learning methods. Four types of machine learning models are applied in this study: random forest (RF), support vector machine (SVM), and deep neural network (DNN), and DNN with differential pooling (DP-DNN). The training and testing data are collected by field experiments under different environmental condition in order to verify the robustness of the machine learning models. The characteristics of reflectance is briefly described, and the results conform with results from previous studies. The performances of the machine learning models are evaluated and compared in terms of both accuracy of prediction and computational complexity. The results indicate that the two DNN models are able to achieve the most accurate prediction among the four machine learning models at the cost of more computation. The SVM model, or the proposed DP-DNN model may be a favorable choice when training time is limited.

show abstract

“…Sun et al studied oils in the laboratory and found that there was a polarization difference between oils and water [104]. This peaked at a look angle of 20 to 30 degrees and at the wavelength of 785 to 880 nm.…”

Section: Other Techniquesmentioning

confidence: 99%

The Challenges of Remotely Measuring Oil Slick Thickness

Fingas¹

2018

Remote Sensing

View full text Add to dashboard Cite

The thickness of oil spills on the sea is an important but poorly studied topic. Means to measure slick thickness are reviewed. More than 30 concepts are summarized. Many of these are judged not to be viable for a variety of scientific reasons. Two means are currently available to remotely measure oil thickness, namely, passive microwave radiometry and time of acoustic travel. Microwave radiometry is commercially developed at this time. Visual means to ascertain oil thickness are restricted by physics to thicknesses smaller than those of rainbow sheens, which rarely occur on large spills, and thin sheen. One can observe that some slicks are not sheen and are probably thicker. These three thickness regimes are not useful to oil spill countermeasures, as most of the oil is contained in the thick portion of a slick, the thickness of which is unknown and ranges over several orders of magnitude. There is a continuing need to measure the thickness of oil spills. This need continues to increase with time, and further research effort is needed. Several viable concepts have been developed but require further work and verification. One of the difficulties is that ground truthing and verification methods are generally not available for most thickness measurement methods.

show abstract

Study on the hyperspectral polarized reflection characteristics of oil slicks on sea surfaces

Cited by 23 publications

References 18 publications

Sentinel-2 image transformation methods for mapping oil spill – A case study with Wakashio oil spill in the Indian Ocean, off Mauritius

Sentinel-2 image transformation methods for mapping oil spill – A case study with Wakashio oil spill in the Indian Ocean, off Mauritius

Identifying Oil Spill Types Based on Remotely Sensed Reflectance Spectra and Multiple Machine Learning Algorithms

The Challenges of Remotely Measuring Oil Slick Thickness

Contact Info

Product

Resources

About