The proposed study focuses on the reported oil spill detection and assessments of oil impacts on marine ecosystems.Five selected oil spills,including those in East China Sea,Balikpapan Bay,Red Sea,Mauritius coast,and ...The proposed study focuses on the reported oil spill detection and assessments of oil impacts on marine ecosystems.Five selected oil spills,including those in East China Sea,Balikpapan Bay,Red Sea,Mauritius coast,and Colombo coast were detected using the Sentinel-1 satellite dataset.Sentinel-2/Landsat 8,and Sentinel-5Precursor(S-5P)satellite datasets were utilized to observe the impacts of oil spills on vegetation cover and air quality respectively.Synthetic aperture radar-based oil spill detection techniques are effective in monitoring oil pollution.Impacts of oil spills on vegetation are monitored via different vegetation indices.The East China Sea spill moved around 190 km from the source point.The area of vegetation cover impacted by the Balikpapan Bay oil spill was 118 km~2.Near real-time data of different toxic gases from S-5P were analyzed for Sri Lanka and the Red Sea using the Google Earth Engine.It is concluded that wind speed was between the range of 3 m/s to 9 m/s that is favorable for the oil spill detection,and it is also observed that wind direction had impacts on oil spill movement as well.Vegetation Indices provide highly reliable results for the four events but the Red Sea oil spill findings were not satisfactory due to low vegetation cover in this area.展开更多
To address the need for the on-site measurement of aging oil, in this paper, we propose an impedance-based microsensor for analyzing the moisture content in engine oil. Using a microfabrication process, we fabricated ...To address the need for the on-site measurement of aging oil, in this paper, we propose an impedance-based microsensor for analyzing the moisture content in engine oil. Using a microfabrication process, we fabricated an interdigitated microelectrode and integrated it with a 3 D-printed microcontainer to produce a microsensor that can detect changes in the permittivity of oil. When the moisture content in oil increases, this sensor can detect the resulting change in the oil impedance, which is related to its permittivity, and then determine the degree to which the oil has aged. The test results show that the proposed microsensor has the advantages of being small and having high sensitivity, good accuracy, and the ability to be combined with hand-held instruments.The proposed method is expected to be used for the rapid, low cost, on-site determination of oil aging.展开更多
The SAR（Synthetic Aperture Radar） has the capabilities for all-weather day and night use. In the case of determining the effects of oil spill dumping, the oil spills areas are shown as dark spots in the SAR images.T...The SAR（Synthetic Aperture Radar） has the capabilities for all-weather day and night use. In the case of determining the effects of oil spill dumping, the oil spills areas are shown as dark spots in the SAR images.Therefore, using SAR data to detect oil spills is becoming progressively popular in operational monitoring, which is useful for oceanic environmental protection and hazard reduction. Research has been conducted on the polarization decomposition and scattering characteristics of oil spills from a scattering matrix using allpolarization of the SAR data, calculation of the polarization parameters, and utilization of the CPD（Co-polarized Phase Difference） of the oil and the sea, in order to extract the oil spill information. This method proves to be effective by combining polarization parameters with the characteristics of oil spill. The results show that when using Bragg, the oil spill backscattering machine with Enopy and a mean scatter α parameter. The oil spill can be successfully identified. However, the parameter mechanism of the oil spill remains unclear. The use of CPD can easily extract oil spill information from the ocean, and the polarization research provides a base for oil spill remote sensing detection.展开更多
In order to meet the requirements for miniaturization detection of oil shale pyrolysis process and solve the problem of low sensitivity of oil and gas detection devices,a small bionic electronic nose system was design...In order to meet the requirements for miniaturization detection of oil shale pyrolysis process and solve the problem of low sensitivity of oil and gas detection devices,a small bionic electronic nose system was designed.Inspired by the working mode of the olfactory receptors in the mouse nasal cavity,the bionic spatial arrangement strategy of the sensor array in the electronic nose chamber was proposed and realized for the first time,the sensor array was used to simulate the distribution of mouse olfactory cells.Using 3D printing technology,a solid model of the electronic nose chamber was manufactured and a comparative test of oil shale pyrolysis gas detection was carried out.The results showed that the proposed spatial arrangement strategy of sensor array inside electronic nose chamber can realize the miniaturization of the electronic nose system,strengthen the detection sensitivity and weaken the mutual interference error.Moreover,it can enhance the recognition rate of the bionic spatial strategy layout,which is higher than the planar layout and spatial comparison layout.This bionic spatial strategy layout combining naive bayes algorithm achieves the highest recognition rate,which is 94.4%.Results obtained from the Computational Fluid Dynamics(CFD)analysis also indicate that the bionic spatial strategy layout can improve the responses of sensors.展开更多
Features of oil spills and look-alikes in polarimetric synthetic aperture radar(SAR)images always play an important role in oil spill detection.Many oil spill detection algorithms have been implemented based on these ...Features of oil spills and look-alikes in polarimetric synthetic aperture radar(SAR)images always play an important role in oil spill detection.Many oil spill detection algorithms have been implemented based on these features.Although environmental factors such as wind speed are important to distinguish oil spills and look-alikes,some oil spill detection algorithms do not consider the environmental factors.To distinguish oil spills and look-alikes more accurately based on environmental factors and image features,a new oil spill detection algorithm based on Dempster-Shafer evidence theory was proposed.The process of oil spill detection taking account of environmental factors was modeled using the subjective Bayesian model.The Faster-region convolutional neural networks(RCNN)model was used for oil spill detection based on the convolution features.The detection results of the two models were fused at decision level using Dempster-Shafer evidence theory.The establishment and test of the proposed algorithm were completed based on our oil spill and look-alike sample database that contains 1798 image samples and environmental information records related to the image samples.The analysis and evaluation of the proposed algorithm shows a good ability to detect oil spills at a higher detection rate,with an identifi cation rate greater than 75%and a false alarm rate lower than 19%from experiments.A total of 12 oil spill SAR images were collected for the validation and evaluation of the proposed algorithm.The evaluation result shows that the proposed algorithm has a good performance on detecting oil spills with an overall detection rate greater than 70%.展开更多
Oil spills pose a major threat to ocean ecosystems and their health. Synthetic aperture radar（SAR） sensors can detect oil spills on the sea surface. These oil spills appear as dark spots in SAR images. However, dark...Oil spills pose a major threat to ocean ecosystems and their health. Synthetic aperture radar（SAR） sensors can detect oil spills on the sea surface. These oil spills appear as dark spots in SAR images. However, dark formations can be caused by a number of phenomena. It is aimed to distinguishing oil spills or look-alike objects. A novel method based on a bidimensional empirical mode decomposition is proposed. The selected dark formations are first decomposed into several bidimensional intrinsic mode functions and the residue. Subsequently, 64 dimension feature sets are calculated using the Hilbert spectral analysis and five new features are extracted with a relief algorithm. Mahalanobis distances are then used for classification. Three data sets containing oil spills or look-alikes are used to test the accuracy rate of the method. The accuracy rate is more than 90%. The experimental results demonstrate that the novel method can detect oil spills validly and accurately.展开更多
To automatically detect oil tanks in polarimetric synthetic aperture radar（SAR） images, a coastal oil tank detection method is proposed based on recognition of T-shaped harbor. First of all, the T-shaped harbor is d...To automatically detect oil tanks in polarimetric synthetic aperture radar（SAR） images, a coastal oil tank detection method is proposed based on recognition of T-shaped harbor. First of all, the T-shaped harbor is detected to locate the region of interest（ROI） of oil tanks. Then all suspicious targets in the ROI are extracted by the segmentation of strong scattering targets and the classifier of H/α. The template targets are selected from the suspicious targets by the combination of a proposed circular degree parameter and the similarity parameter（SP） of the polarimetric coherency matrix. Finally, oil tanks are detected according to the statistics of the similarity parameter between each suspicious target and template targets in ROI. Polarimetric SAR data acquired by RADARSAT-2 over Berkeley and Singapore areas are used for testing. Experiment results show that most of the targets are correctly detected and the overall detection rate is close to 80%.The false rate is effectively reduced by the proposed algorithm compared with the method without T-shaped harbor recognition.展开更多
This paper presents a novel laser⁃induced fluorescence(LIF)Lidar system for marine oil spilling detection.A bifurcated Y⁃type optical fiber and an optical collimating lens compose a coaxial configuration transceiver f...This paper presents a novel laser⁃induced fluorescence(LIF)Lidar system for marine oil spilling detection.A bifurcated Y⁃type optical fiber and an optical collimating lens compose a coaxial configuration transceiver for this LIF⁃Lidar system.This LIF⁃Lidar system was further applied to measure the excitation spectra from floating oil slicks with different thicknesses on top of seawater at different distances.The system presents several advantages such as compact structure,stable optical path,and convenient operation,which offers a wide application prospect in ocean exploration.展开更多
Compared with single-polarized synthetic aperture radar （SAR） images, full polarimetric SAIl images contain not only geometrical and backward scattering characteristics, but also the polarization features of the sca...Compared with single-polarized synthetic aperture radar （SAR） images, full polarimetric SAIl images contain not only geometrical and backward scattering characteristics, but also the polarization features of the scattering targets. Therefore, the polarimetric SAR has more advantages for oil spill detection on the sea surface. As a crucial step in the oil spill detection, a feature extraction directly influences the accuracy of oil spill discrimination. The polarimetric features of sea oil spills, such as polarimetric entropy, average scatter angle, in the full polarimetric SAR images are analyzed firstly. And a new polarimetric parameter P which reflects the proportion between Bragg and specular scattering signals is proposed. In order to investigate the capability of the polarimetric features for observing an oil spill, systematic comparisons and analyses of the multipolarization features are provided on the basis of the full polarimetric SAR images acquired by SIR-C/X-SAR and Radarsat-2. The experiment results show that in C-band SAR images the oil spills can be detected more easily than in L-band SAR images under low to moderate wind speed conditions. Moreover, it also finds that the new polarimetric parameter is sensitive to the sea surface scattering mechanisms. And the experiment results demonstrate that the new polarimetric parameter and pedestal height perform better than other polarimetric parameters for the oil spill detection in the C-band SAR images.展开更多
A fully distributed optical fiber sensor （DOFS） for monitoring long-distance oil pipeline health is proposed based on optical time domain reflectometry （OTDR）. A smart and sensitive optical fiber cable is installe...A fully distributed optical fiber sensor （DOFS） for monitoring long-distance oil pipeline health is proposed based on optical time domain reflectometry （OTDR）. A smart and sensitive optical fiber cable is installed along the pipeline acting as a sensor, The experiments show that the cable swells when exposed to oil and induced additional bending losses inside the fiber, and the optical attenuation of the fiber coated by a thin skin with periodical hardness is sensitive to deformation and vibration caused by oil leakage, tampering, or mechanical impact. The region where the additional attenuation occurred is detected and located by DOFS based on OTDR, the types of pipeline accidents are identified according to the characteristics of transmitted optical power received by an optical power meter, Another prototype of DOFS based on a forward traveling frequency-modulated continuous-wave （FMCW） is also proposed to monitor pipeline. The advantages and disadvantages of DOFSs based on OTDR and FMCW are discussed. The experiments show that DOFSs are capable of detecting and locating distant oil pipeline leakages and damages in real time with an estimated precision of ten meters over tens of kilometers.展开更多
This paper proposes an automatic detection of oil spills on SAR （synthetic aperture radar） images using DE （differential evolution）, neutral network and BP （back propagation） algorithm. Here, DE and BP are combi...This paper proposes an automatic detection of oil spills on SAR （synthetic aperture radar） images using DE （differential evolution）, neutral network and BP （back propagation） algorithm. Here, DE and BP are combined to train a multilayer perceptron （MLP） network for achieving the global extreme with a better convergence speed. The input data of neural networks are the geometrical characteristics ofoil spills （e.g. area, perimeter, complexity） and the physical behavior ofoil spills （e,g. mean or max backscatter value, standard deviation of the dark formation）. The out data are oil spill or look-alike. We experiment ALOS/PALSAR and EnviSAT ASAR on East sea area of Viet Nam. The experimental results show that the combination algorithm converges faster and has significantly better capability of avoiding local optima.展开更多
Ⅰ The Indexes of Detecting Oil and Gas Resources The deeply buried reservoir which in a dynamic equilibrium state has a great pressure inside, and between it and earth surface there is a great difference of pressure....Ⅰ The Indexes of Detecting Oil and Gas Resources The deeply buried reservoir which in a dynamic equilibrium state has a great pressure inside, and between it and earth surface there is a great difference of pressure. Therefore the hydrocarbon must spread and move vertically to the surface along the pressure gradient orientation. Hydrocarbons in the reservoir along some small rifts, cracks, joints and cleavages penetrate the overlying strata and seepage onto the surface. Thus the hydrocarbons become unvisble oil and gas signs. This process is called the phenomena of hydrocarbon microseepage of reservoir. Hydrocarbons microseepage in the process展开更多
Sentinel1 data were applied to monitor oil pollution in the Persian Gulf in the vicinity of a Hormuz Strait.Radar images were recorded by sentinel 1 A from January to July 2020.To analyze radar images,two different al...Sentinel1 data were applied to monitor oil pollution in the Persian Gulf in the vicinity of a Hormuz Strait.Radar images were recorded by sentinel 1 A from January to July 2020.To analyze radar images,two different algorithms were used for oil spill detection and wind field estimation.Based on the result,amplitude VV can be the best band to detect oil spills while intensity VV can be a suitable choice for wind field estimation.Testing many cases,the best threshold shift and minimum cluster size were con-sidered 1.9 dB and 0.08 km respectively for oil spill algorithm compared to 10 km window size for wind field estimation.The outputs revealed that the March was the most polluted month with this contami-nation clearly found in Qeshm island and the southern part of the Hormuz Strait.We tracked the path of oil slicks when moving Oman coast.The calculation showed that the volume of some slicks can be up to 160 km^(2).The wind field estimation algorithm indicated that the mean wind speed can be 5 m/s while this value in spring was greater than in winter.Further,some physical oceanography processes play an important role in propagating oil spill in the Persian Gulf such as wind,wave,the frontal system.In the Persian Gulf,the baroclinic instability is as a result of inflow and outflow current in this basin,leading to some meso-scale eddies which have an important role in propagating oil spills.Due to the importance of wave breaking in this region,the entrainment rate from breaking wave was estimated at 9×10^(−7) s^(−1) with weber number and Ohnesorge number 5135 and 6.9 respectively.展开更多
We present a method for detecting oil spills in a complex scene of SAR imagery,including segmenting oil spills,and avoiding false alarms.Segmentation is carried out using a multi-time and multi-hierarchical method by ...We present a method for detecting oil spills in a complex scene of SAR imagery,including segmenting oil spills,and avoiding false alarms.Segmentation is carried out using a multi-time and multi-hierarchical method by dividing the complex sea surface into bright sea and dark sea.Gray-based and edge-based segmentations are done to extract oil spills from bright and dark sea,respectively.The proposed method can extract complete oil spills,obtain better visual results,and increase detection probability more accurately than the traditional method.Based on the surrounding features and the oil spills’features,dark land spots and low contrast dark spots are removed efficiently,thus reducing false alarms.The experimental results demonstrate that the proposed algorithm has fast computation speed,high detection accuracy,and is very useful and effective for detecting oil spills in SAR imagery.展开更多
Oil spill monitoring in remote sensing field has become a very popular technology to detect the spatial distribution of polluted regions.However,previous studies mainly focus on the supervised detection technologies,w...Oil spill monitoring in remote sensing field has become a very popular technology to detect the spatial distribution of polluted regions.However,previous studies mainly focus on the supervised detection technologies,which requires a large number of high-quality training set.To solve this problem,we propose a self-supervised learning method to learn the deep neural network from unlabelled hyperspectral data for oil spill detection,which consists of three parts:data augmentation,unsupervised deep feature learning,and oil spill detection network.First,the original image is augmented with spectral and spatial transformation to improve robustness of the self-supervised model.Then,the deep neural networks are trained on the augmented data without label information to produce the high-level semantic features.Finally,the pre-trained parameters are transferred to establish a neural network classifier to obtain the detection result,where a contrastive loss is developed to fine-tune the learned parameters so as to improve the generalization ability of the proposed method.Experiments performed on ten oil spill datasets reveal that the proposed method obtains promising detection performance with respect to other state-of-the-art hyperspectral detection approaches.展开更多
Hybrid-polarimetric SAR(synthetic aperture radar) is a new SAR mode, with relatively simple architecture, low cost, and wide swath, which will be carried by several Earth-observing systems from now to the near future....Hybrid-polarimetric SAR(synthetic aperture radar) is a new SAR mode, with relatively simple architecture, low cost, and wide swath, which will be carried by several Earth-observing systems from now to the near future. Here, we show how the second Stokes parameter of hybrid-polarimetric SAR can be employed to detect oil on the ocean surface using the classic well-known Otsu threshold methodology, in relation to contributions from different polarizations and dampening effects on backscatter intensity, neglecting the specific scattering mechanisms and oil types for an oil-covered surface. The detection methodology is demonstrated to be reliable in three example cases: oil-on-water experiments conducted by the Norwegian Clean Seas Association, natural oil seeps from the Gulf of Mexico, and observations from the Deep Water Horizon oil spill disaster in 2010.展开更多
文摘The proposed study focuses on the reported oil spill detection and assessments of oil impacts on marine ecosystems.Five selected oil spills,including those in East China Sea,Balikpapan Bay,Red Sea,Mauritius coast,and Colombo coast were detected using the Sentinel-1 satellite dataset.Sentinel-2/Landsat 8,and Sentinel-5Precursor(S-5P)satellite datasets were utilized to observe the impacts of oil spills on vegetation cover and air quality respectively.Synthetic aperture radar-based oil spill detection techniques are effective in monitoring oil pollution.Impacts of oil spills on vegetation are monitored via different vegetation indices.The East China Sea spill moved around 190 km from the source point.The area of vegetation cover impacted by the Balikpapan Bay oil spill was 118 km~2.Near real-time data of different toxic gases from S-5P were analyzed for Sri Lanka and the Red Sea using the Google Earth Engine.It is concluded that wind speed was between the range of 3 m/s to 9 m/s that is favorable for the oil spill detection,and it is also observed that wind direction had impacts on oil spill movement as well.Vegetation Indices provide highly reliable results for the four events but the Red Sea oil spill findings were not satisfactory due to low vegetation cover in this area.
基金the financial support provided by the National Natural Science Foundation of China (NSFC No. U1733120, 61601469, 61674114, 61701475, 91743110, 21861132001)National Key R&D Program of China (2017YFF0204604, 2018YFE0118700)+4 种基金Tianjin Applied Basic Research and Advanced Technology (17JCJQJC43600)the 111 Project (B07014)the Initial Scientific Research Fund of State Key Laboratory of Precision Measuring Technology and Instruments, Tianjin University (No. Pilq1902)the Foundation for Talent Scientists of Nanchang Institute for Micro-technology of Tianjin Universitythe Open Research Fund of Key Laboratory of MEMS of Ministry of Education, Southeast University。
文摘To address the need for the on-site measurement of aging oil, in this paper, we propose an impedance-based microsensor for analyzing the moisture content in engine oil. Using a microfabrication process, we fabricated an interdigitated microelectrode and integrated it with a 3 D-printed microcontainer to produce a microsensor that can detect changes in the permittivity of oil. When the moisture content in oil increases, this sensor can detect the resulting change in the oil impedance, which is related to its permittivity, and then determine the degree to which the oil has aged. The test results show that the proposed microsensor has the advantages of being small and having high sensitivity, good accuracy, and the ability to be combined with hand-held instruments.The proposed method is expected to be used for the rapid, low cost, on-site determination of oil aging.
基金The National Natural Science Foundation of China under contract No.41376183the High Resolution Images Services Special Projection for ocean applicationsthe Oceanography Public Welfare Scientific Research Project-Marine of China under contract No.201205012
文摘The SAR（Synthetic Aperture Radar） has the capabilities for all-weather day and night use. In the case of determining the effects of oil spill dumping, the oil spills areas are shown as dark spots in the SAR images.Therefore, using SAR data to detect oil spills is becoming progressively popular in operational monitoring, which is useful for oceanic environmental protection and hazard reduction. Research has been conducted on the polarization decomposition and scattering characteristics of oil spills from a scattering matrix using allpolarization of the SAR data, calculation of the polarization parameters, and utilization of the CPD（Co-polarized Phase Difference） of the oil and the sea, in order to extract the oil spill information. This method proves to be effective by combining polarization parameters with the characteristics of oil spill. The results show that when using Bragg, the oil spill backscattering machine with Enopy and a mean scatter α parameter. The oil spill can be successfully identified. However, the parameter mechanism of the oil spill remains unclear. The use of CPD can easily extract oil spill information from the ocean, and the polarization research provides a base for oil spill remote sensing detection.
基金This work was supported by the National Natural Science Foundof China(51875245)the Science-Technology Development Plan Project of Jilin Province(20190303012SF,20190303118SF and 20190201019JC)+2 种基金the Special Project of Industrial Technology Research and Development of Jilin Province(2018C036-2)the“13th Five-Year Plan”Scientific Research Foundation of the Education Department of Jilin Province(JJKH20201000KJ and JJKH20201019KJ)the Fundamental Research Funds for the Central Universities.
文摘In order to meet the requirements for miniaturization detection of oil shale pyrolysis process and solve the problem of low sensitivity of oil and gas detection devices,a small bionic electronic nose system was designed.Inspired by the working mode of the olfactory receptors in the mouse nasal cavity,the bionic spatial arrangement strategy of the sensor array in the electronic nose chamber was proposed and realized for the first time,the sensor array was used to simulate the distribution of mouse olfactory cells.Using 3D printing technology,a solid model of the electronic nose chamber was manufactured and a comparative test of oil shale pyrolysis gas detection was carried out.The results showed that the proposed spatial arrangement strategy of sensor array inside electronic nose chamber can realize the miniaturization of the electronic nose system,strengthen the detection sensitivity and weaken the mutual interference error.Moreover,it can enhance the recognition rate of the bionic spatial strategy layout,which is higher than the planar layout and spatial comparison layout.This bionic spatial strategy layout combining naive bayes algorithm achieves the highest recognition rate,which is 94.4%.Results obtained from the Computational Fluid Dynamics(CFD)analysis also indicate that the bionic spatial strategy layout can improve the responses of sensors.
基金Supported by the National Key R&D Program of China(No.2017YFC1405600)the National Natural Science Foundation of China(Nos.42076197,41576032)the Major Program for the International Cooperation of the Chinese Academy of Sciences(No.133337KYSB20160002)。
文摘Features of oil spills and look-alikes in polarimetric synthetic aperture radar(SAR)images always play an important role in oil spill detection.Many oil spill detection algorithms have been implemented based on these features.Although environmental factors such as wind speed are important to distinguish oil spills and look-alikes,some oil spill detection algorithms do not consider the environmental factors.To distinguish oil spills and look-alikes more accurately based on environmental factors and image features,a new oil spill detection algorithm based on Dempster-Shafer evidence theory was proposed.The process of oil spill detection taking account of environmental factors was modeled using the subjective Bayesian model.The Faster-region convolutional neural networks(RCNN)model was used for oil spill detection based on the convolution features.The detection results of the two models were fused at decision level using Dempster-Shafer evidence theory.The establishment and test of the proposed algorithm were completed based on our oil spill and look-alike sample database that contains 1798 image samples and environmental information records related to the image samples.The analysis and evaluation of the proposed algorithm shows a good ability to detect oil spills at a higher detection rate,with an identifi cation rate greater than 75%and a false alarm rate lower than 19%from experiments.A total of 12 oil spill SAR images were collected for the validation and evaluation of the proposed algorithm.The evaluation result shows that the proposed algorithm has a good performance on detecting oil spills with an overall detection rate greater than 70%.
基金The National Science and Technology Support Project under contract No.2014BAB12B02the Natural Science Foundation of Liaoning Province under contract No.201602042
文摘Oil spills pose a major threat to ocean ecosystems and their health. Synthetic aperture radar（SAR） sensors can detect oil spills on the sea surface. These oil spills appear as dark spots in SAR images. However, dark formations can be caused by a number of phenomena. It is aimed to distinguishing oil spills or look-alike objects. A novel method based on a bidimensional empirical mode decomposition is proposed. The selected dark formations are first decomposed into several bidimensional intrinsic mode functions and the residue. Subsequently, 64 dimension feature sets are calculated using the Hilbert spectral analysis and five new features are extracted with a relief algorithm. Mahalanobis distances are then used for classification. Three data sets containing oil spills or look-alikes are used to test the accuracy rate of the method. The accuracy rate is more than 90%. The experimental results demonstrate that the novel method can detect oil spills validly and accurately.
基金supported by the National Key R&D Program of China(2017YFB0502700)the National Natural Science Foundation of China(61490693+3 种基金61771043)the High-Resolution Earth Observation Systems(41-Y20A14-9001-15/1630-Y20A12-9004-15/1630-Y20A10-9001-15/16)
文摘To automatically detect oil tanks in polarimetric synthetic aperture radar（SAR） images, a coastal oil tank detection method is proposed based on recognition of T-shaped harbor. First of all, the T-shaped harbor is detected to locate the region of interest（ROI） of oil tanks. Then all suspicious targets in the ROI are extracted by the segmentation of strong scattering targets and the classifier of H/α. The template targets are selected from the suspicious targets by the combination of a proposed circular degree parameter and the similarity parameter（SP） of the polarimetric coherency matrix. Finally, oil tanks are detected according to the statistics of the similarity parameter between each suspicious target and template targets in ROI. Polarimetric SAR data acquired by RADARSAT-2 over Berkeley and Singapore areas are used for testing. Experiment results show that most of the targets are correctly detected and the overall detection rate is close to 80%.The false rate is effectively reduced by the proposed algorithm compared with the method without T-shaped harbor recognition.
基金Sponsored by the National Natural Science Foundation of China(Grant No.61605033)the Natural Science Foundation of Shandong Province(Grant No.ZR2016FQ24)+1 种基金the Taishan Blue Industry Leadership Program,Project of Shandong Province(Grant No.[2015]1363)the Fundamental Research Funds for the Central Universities(Grant No.HIT.NSRIF.201719).
文摘This paper presents a novel laser⁃induced fluorescence(LIF)Lidar system for marine oil spilling detection.A bifurcated Y⁃type optical fiber and an optical collimating lens compose a coaxial configuration transceiver for this LIF⁃Lidar system.This LIF⁃Lidar system was further applied to measure the excitation spectra from floating oil slicks with different thicknesses on top of seawater at different distances.The system presents several advantages such as compact structure,stable optical path,and convenient operation,which offers a wide application prospect in ocean exploration.
基金The National Natural Science Foundation of China under contract Nos 41576170 and 41376179the Public Science and Technology Research Funds Projects of Ocean(Ocean University of China) under contract No.2013418025-2
文摘Compared with single-polarized synthetic aperture radar （SAR） images, full polarimetric SAIl images contain not only geometrical and backward scattering characteristics, but also the polarization features of the scattering targets. Therefore, the polarimetric SAR has more advantages for oil spill detection on the sea surface. As a crucial step in the oil spill detection, a feature extraction directly influences the accuracy of oil spill discrimination. The polarimetric features of sea oil spills, such as polarimetric entropy, average scatter angle, in the full polarimetric SAR images are analyzed firstly. And a new polarimetric parameter P which reflects the proportion between Bragg and specular scattering signals is proposed. In order to investigate the capability of the polarimetric features for observing an oil spill, systematic comparisons and analyses of the multipolarization features are provided on the basis of the full polarimetric SAR images acquired by SIR-C/X-SAR and Radarsat-2. The experiment results show that in C-band SAR images the oil spills can be detected more easily than in L-band SAR images under low to moderate wind speed conditions. Moreover, it also finds that the new polarimetric parameter is sensitive to the sea surface scattering mechanisms. And the experiment results demonstrate that the new polarimetric parameter and pedestal height perform better than other polarimetric parameters for the oil spill detection in the C-band SAR images.
基金This project is supported by R&D Foundation of National Petroleum Corporation (CNPC) of China(No.2001411-4).
文摘A fully distributed optical fiber sensor （DOFS） for monitoring long-distance oil pipeline health is proposed based on optical time domain reflectometry （OTDR）. A smart and sensitive optical fiber cable is installed along the pipeline acting as a sensor, The experiments show that the cable swells when exposed to oil and induced additional bending losses inside the fiber, and the optical attenuation of the fiber coated by a thin skin with periodical hardness is sensitive to deformation and vibration caused by oil leakage, tampering, or mechanical impact. The region where the additional attenuation occurred is detected and located by DOFS based on OTDR, the types of pipeline accidents are identified according to the characteristics of transmitted optical power received by an optical power meter, Another prototype of DOFS based on a forward traveling frequency-modulated continuous-wave （FMCW） is also proposed to monitor pipeline. The advantages and disadvantages of DOFSs based on OTDR and FMCW are discussed. The experiments show that DOFSs are capable of detecting and locating distant oil pipeline leakages and damages in real time with an estimated precision of ten meters over tens of kilometers.
文摘This paper proposes an automatic detection of oil spills on SAR （synthetic aperture radar） images using DE （differential evolution）, neutral network and BP （back propagation） algorithm. Here, DE and BP are combined to train a multilayer perceptron （MLP） network for achieving the global extreme with a better convergence speed. The input data of neural networks are the geometrical characteristics ofoil spills （e.g. area, perimeter, complexity） and the physical behavior ofoil spills （e,g. mean or max backscatter value, standard deviation of the dark formation）. The out data are oil spill or look-alike. We experiment ALOS/PALSAR and EnviSAT ASAR on East sea area of Viet Nam. The experimental results show that the combination algorithm converges faster and has significantly better capability of avoiding local optima.
文摘Ⅰ The Indexes of Detecting Oil and Gas Resources The deeply buried reservoir which in a dynamic equilibrium state has a great pressure inside, and between it and earth surface there is a great difference of pressure. Therefore the hydrocarbon must spread and move vertically to the surface along the pressure gradient orientation. Hydrocarbons in the reservoir along some small rifts, cracks, joints and cleavages penetrate the overlying strata and seepage onto the surface. Thus the hydrocarbons become unvisble oil and gas signs. This process is called the phenomena of hydrocarbon microseepage of reservoir. Hydrocarbons microseepage in the process
文摘Sentinel1 data were applied to monitor oil pollution in the Persian Gulf in the vicinity of a Hormuz Strait.Radar images were recorded by sentinel 1 A from January to July 2020.To analyze radar images,two different algorithms were used for oil spill detection and wind field estimation.Based on the result,amplitude VV can be the best band to detect oil spills while intensity VV can be a suitable choice for wind field estimation.Testing many cases,the best threshold shift and minimum cluster size were con-sidered 1.9 dB and 0.08 km respectively for oil spill algorithm compared to 10 km window size for wind field estimation.The outputs revealed that the March was the most polluted month with this contami-nation clearly found in Qeshm island and the southern part of the Hormuz Strait.We tracked the path of oil slicks when moving Oman coast.The calculation showed that the volume of some slicks can be up to 160 km^(2).The wind field estimation algorithm indicated that the mean wind speed can be 5 m/s while this value in spring was greater than in winter.Further,some physical oceanography processes play an important role in propagating oil spill in the Persian Gulf such as wind,wave,the frontal system.In the Persian Gulf,the baroclinic instability is as a result of inflow and outflow current in this basin,leading to some meso-scale eddies which have an important role in propagating oil spills.Due to the importance of wave breaking in this region,the entrainment rate from breaking wave was estimated at 9×10^(−7) s^(−1) with weber number and Ohnesorge number 5135 and 6.9 respectively.
基金supported by the National Natural Science Foundation of China(Grant Nos.61171194,61120106004)"111"Project of China(Grant No.B14010)
文摘We present a method for detecting oil spills in a complex scene of SAR imagery,including segmenting oil spills,and avoiding false alarms.Segmentation is carried out using a multi-time and multi-hierarchical method by dividing the complex sea surface into bright sea and dark sea.Gray-based and edge-based segmentations are done to extract oil spills from bright and dark sea,respectively.The proposed method can extract complete oil spills,obtain better visual results,and increase detection probability more accurately than the traditional method.Based on the surrounding features and the oil spills’features,dark land spots and low contrast dark spots are removed efficiently,thus reducing false alarms.The experimental results demonstrate that the proposed algorithm has fast computation speed,high detection accuracy,and is very useful and effective for detecting oil spills in SAR imagery.
基金supported by the National Natural Science Foundation of China (Grant No. 61890962 and 61871179)the Scientific Research Project of Hunan Education Department (Grant No. 19B105)+3 种基金the Natural Science Foundation of Hunan Province (Grant Nos. 2019JJ50036 and 2020GK2038)the National Key Research and Development Project (Grant No. 2021YFA0715203)the Hunan Provincial Natural Science Foundation for Distinguished Young Scholars (Grant No. 2021JJ022)the Huxiang Young Talents Science and Technology Innovation Program (Grant No. 2020RC3013)
文摘Oil spill monitoring in remote sensing field has become a very popular technology to detect the spatial distribution of polluted regions.However,previous studies mainly focus on the supervised detection technologies,which requires a large number of high-quality training set.To solve this problem,we propose a self-supervised learning method to learn the deep neural network from unlabelled hyperspectral data for oil spill detection,which consists of three parts:data augmentation,unsupervised deep feature learning,and oil spill detection network.First,the original image is augmented with spectral and spatial transformation to improve robustness of the self-supervised model.Then,the deep neural networks are trained on the augmented data without label information to produce the high-level semantic features.Finally,the pre-trained parameters are transferred to establish a neural network classifier to obtain the detection result,where a contrastive loss is developed to fine-tune the learned parameters so as to improve the generalization ability of the proposed method.Experiments performed on ten oil spill datasets reveal that the proposed method obtains promising detection performance with respect to other state-of-the-art hyperspectral detection approaches.
基金supported by the National Natural Science Foundation of China(Grant No.41306189)the Knowledge Innovative Program of the Chinese Academy of Sciences+2 种基金the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)the Canadian Program on Energy Research and Developmentthe Canadian Space Agency GRIP initiative
文摘Hybrid-polarimetric SAR(synthetic aperture radar) is a new SAR mode, with relatively simple architecture, low cost, and wide swath, which will be carried by several Earth-observing systems from now to the near future. Here, we show how the second Stokes parameter of hybrid-polarimetric SAR can be employed to detect oil on the ocean surface using the classic well-known Otsu threshold methodology, in relation to contributions from different polarizations and dampening effects on backscatter intensity, neglecting the specific scattering mechanisms and oil types for an oil-covered surface. The detection methodology is demonstrated to be reliable in three example cases: oil-on-water experiments conducted by the Norwegian Clean Seas Association, natural oil seeps from the Gulf of Mexico, and observations from the Deep Water Horizon oil spill disaster in 2010.
