22.5.7 Oil Slicks, Oil Spills, Water Areas

Chapter Contents (Back)
Oil Slicks. Oil Spills. Water Detection.

Derrode, S.[Stéphane], Mercier, G.[Grégoire],
Unsupervised multiscale oil slick segmentation from SAR images using a vector HMC model,
PR(40), No. 3, March 2007, pp. 1135-1147.
Elsevier DOI 0611
Oil slick detection; Multiscale wavelet analysis; Hidden Markov chain; Unsupervised segmentation BibRef

Chang, L.[Lena], Tang, Z.S., Chang, S.H., Chang, Y.L.[Yang-Lang],
A region-based GLRT detection of oil spills in SAR images,
PRL(29), No. 14, October 2008, pp. 1915-1923.
Elsevier DOI 0804
Oil spills; SAR image; Image segmentation; Generalizes likelihood ratio test (GLRT); Constant false alarm ratio (CFAR) BibRef

Marques, R.C.P., de Medeiros, F.N.S.[Fátima N.S.], Ushizima, D.M.,
Target Detection in SAR Images Based on a Level Set Approach,
SMC-C(39), No. 2, March 2009, pp. 214-222.

de A. Lopes, D.F.[Darby F.], Ramalho, G.L.B.[Geraldo L.B.], de Medeiros, F.N.S.[Fátima N.S.], Costa, R.C.S.[Rodrigo C.S.], Araújo, R.T.S.[Regia T. S.],
Combining Features to Improve Oil Spill Classification in SAR Images,
Springer DOI 0608

Ramalho, G.L.B.[Geraldo L.B.], de Medeiros, F.N.S.[Fátima N.S.],
Improving Reliability of Oil Spill Detection Systems Using Boosting for High-Level Feature Selection,
Springer DOI 0708
Using Boosting to Improve Oil Spill Detection in SAR Images,
ICPR06(II: 1066-1069).

Ramsey, III, E., Rangoonwala, A., Suzuoki, Y., Jones, C.,
Oil Detection in a Coastal Marsh with Polarimetric Synthetic Aperture Radar (SAR),
RS(3), No. 12, December 2011, pp. 2630-2662.
DOI Link 1203

Velotto, D., Migliaccio, M., Nunziata, F., Lehner, S.,
Dual-Polarized TerraSAR-X Data for Oil-Spill Observation,
GeoRS(49), No. 12, December 2011, pp. 4751-4762.

Topouzelis, K.[Konstantinos], Psyllos, A.[Apostolos],
Oil spill feature selection and classification using decision tree forest on SAR image data,
PandRS(68), No. 1, March 2012, pp. 135-143.
Elsevier DOI 1204
Oil spill; Decision forest; Feature selection; SAR; Classification; Machine learning BibRef

Solberg, A.H.S.,
Remote Sensing of Ocean Oil-Spill Pollution,
PIEEE(100), No. 10, October 2012, pp. 2931-2945.

Vespe, M., Greidanus, H.,
SAR Image Quality Assessment and Indicators for Vessel and Oil Spill Detection,
GeoRS(50), No. 11, November 2012, pp. 4726-4734.

Nunziata, F.[Ferdinando], Gambardella, A.[Attilio], Migliaccio, M.[Maurizio],
On the degree of polarization for SAR sea oil slick observation,
PandRS(78), No. 1, April 2013, pp. 41-49.
Elsevier DOI 1304
Polarimetry; Synthetic Aperture Radar (SAR); Oil pollution; Degree of polarization; Coastal water BibRef

Salberg, A.B.[Arnt-Børre], Rudjord, O., Solberg, A.H.S.,
Oil Spill Detection in Hybrid-Polarimetric SAR Images,
GeoRS(52), No. 10, October 2014, pp. 6521-6533.
Correlation BibRef

Brekke, C.[Camilla], Solberg, A.H.S.[Anne H.S.],
Feature Extraction for Oil Spill Detection Based on SAR Images,
Springer DOI 0506

de Carolis, G., Adamo, M., Pasquariello, G.,
On the Estimation of Thickness of Marine Oil Slicks From Sun-Glittered, Near-Infrared MERIS and MODIS Imagery: The Lebanon Oil Spill Case Study,
GeoRS(52), No. 1, January 2014, pp. 559-573.
infrared imaging BibRef

Bandiera, F., Masciullo, A., Ricci, G.,
A Bayesian Approach to Oil Slicks Edge Detection Based on SAR Data,
GeoRS(52), No. 5, May 2014, pp. 2901-2909.
Azimuth BibRef

Skrunes, S., Brekke, C., Eltoft, T.,
Characterization of Marine Surface Slicks by Radarsat-2 Multipolarization Features,
GeoRS(52), No. 9, Sept 2014, pp. 5302-5319.
marine pollution BibRef

Niclòs, R.[Raquel], Dona, C., Valor, E.[Enric], Bisquert, M.,
Thermal-Infrared Spectral and Angular Characterization of Crude Oil and Seawater Emissivities for Oil Slick Identification,
GeoRS(52), No. 9, Sept 2014, pp. 5387-5395.
crude oil BibRef

Etellisi, E.A.[Ehab A.], Deng, Y.M.[Yi-Ming],
Oil spill detection: imaging system modeling and advanced image processing using optimized SDC algorithm,
SIViP(8), No. 8, November 2014, pp. 1405-1419.
WWW Link. 1411

Wei, E.B.[En-Bo], Liu, S.B.[Shu-Bo], Wang, Z.Z.[Zhen-Zhan], Tong, X.L.[Xiao-Lin], Dong, S.[Shuai], Li, B.[Bin], Liu, J.Y.[Jing-Yi],
Emissivity Measurements of Foam-Covered Water Surface at L-Band for Low Water Temperatures,
RS(6), No. 11, 2014, pp. 10913-10930.
DOI Link 1412

Taravat, A., Latini, D., del Frate, F.,
Fully Automatic Dark-Spot Detection From SAR Imagery With the Combination of Nonadaptive Weibull Multiplicative Model and Pulse-Coupled Neural Networks,
GeoRS(52), No. 5, May 2014, pp. 2427-2435.
Earlier: A1, A3, Only:
Weibull Multiplicative Model and Machine Learning Models for Full-Automatic Dark-Spot Detection from SAR Images,
HTML Version. 1311
Feature extraction. Oil spill monitoring. BibRef

Pisano, A.[Andrea], Bignami, F.[Francesco], Santoleri, R.[Rosalia],
Oil Spill Detection in Glint-Contaminated Near-Infrared MODIS Imagery,
RS(7), No. 1, 2015, pp. 1112-1134.
DOI Link 1502

Skrunes, S., Brekke, C., Eltoft, T., Kudryavtsev, V.,
Comparing Near-Coincident C- and X-Band SAR Acquisitions of Marine Oil Spills,
GeoRS(53), No. 4, April 2015, pp. 1958-1975.
marine pollution BibRef

Suresh, G., Melsheimer, C., Korber, J.H., Bohrmann, G.,
Automatic Estimation of Oil Seep Locations in Synthetic Aperture Radar Images,
GeoRS(53), No. 8, August 2015, pp. 4218-4230.
geophysical image processing BibRef

Yan, J.N.[Ji-Ning], Wang, L.Z.[Li-Zhe], Chen, L.J.[La-Jiao], Zhao, L.J.[Ling-Jun], Huang, B.M.[Bo-Min],
A Dynamic Remote Sensing Data-Driven Approach for Oil Spill Simulation in the Sea,
RS(7), No. 6, 2015, pp. 7105.
DOI Link 1507

Ramsey, E.[Elijah], Rangoonwala, A.[Amina], Jones, C.E.[Cathleen E.],
Structural Classification of Marshes with Polarimetric SAR Highlighting the Temporal Mapping of Marshes Exposed to Oil,
RS(7), No. 9, 2015, pp. 11295.
DOI Link 1511

Rapaport, T.[Tal], Hochberg, U.[Uri], Shoshany, M.[Maxim], Karnieli, A.[Arnon], Rachmilevitch, S.[Shimon],
Combining leaf physiology, hyperspectral imaging and partial least squares-regression (PLS-R) for grapevine water status assessment,
PandRS(109), No. 1, 2015, pp. 88-97.
Elsevier DOI 1512
Grapevine BibRef

Zhang, H.[Hao], Mendoza-Sanchez, I., Miller, E.L., Abriola, L.M.,
Manifold Regression Framework for Characterizing Source Zone Architecture,
GeoRS(54), No. 1, January 2016, pp. 3-17.
contaminated site remediation. Other contamination. BibRef

Konik, M., Bradtke, K.,
Object-oriented approach to oil spill detection using ENVISAT ASAR images,
PandRS(118), No. 1, 2016, pp. 37-52.
Elsevier DOI 1606
Remote sensing BibRef

Buono, A., Nunziata, F., Migliaccio, M., Li, X.,
Polarimetric Analysis of Compact-Polarimetry SAR Architectures for Sea Oil Slick Observation,
GeoRS(54), No. 10, October 2016, pp. 5862-5874.
oil pollution BibRef

Mityagina, M.[Marina], Lavrova, O.[Olga],
Satellite Survey of Inner Seas: Oil Pollution in the Black and Caspian Seas,
RS(8), No. 10, 2016, pp. 875.
DOI Link 1609

Lavrova, O.[Olga], Mityagina, M.[Marina],
Satellite Survey of Internal Waves in the Black and Caspian Seas,
RS(9), No. 9, 2017, pp. xx-yy.
DOI Link 1711

Otremba, Z.[Zbigniew],
Oil Droplet Clouds Suspended in the Sea: Can They Be Remotely Detected?,
RS(8), No. 10, 2016, pp. 857.
DOI Link 1609

de Maio, A., Orlando, D., Pallotta, L., Clemente, C.,
A Multifamily GLRT for Oil Spill Detection,
GeoRS(55), No. 1, January 2017, pp. 63-79.
marine pollution BibRef

Lacava, T.[Teodosio], Ciancia, E.[Emanuele], Coviello, I.[Irina], di Polito, C.[Carmine], Grimaldi, C.S.L.[Caterina S. L.], Pergola, N.[Nicola], Satriano, V.[Valeria], Temimi, M.[Marouane], Zhao, J.[Jun], Tramutoli, V.[Valerio],
A MODIS-Based Robust Satellite Technique (RST) for Timely Detection of Oil Spilled Areas,
RS(9), No. 2, 2017, pp. xx-yy.
DOI Link 1703

Khanna, S.[Shruti], Santos, M.J.[Maria J.], Koltunov, A.[Alexander], Shapiro, K.D.[Kristen D.], Lay, M.[Mui], Ustin, S.L.[Susan L.],
Marsh Loss Due to Cumulative Impacts of Hurricane Isaac and the Deepwater Horizon Oil Spill in Louisiana,
RS(9), No. 2, 2017, pp. xx-yy.
DOI Link 1703

Mager, A.[Alexander], Wirkus, L.[Lars], Schoepfer, E.[Elisabeth],
Impact Assessment of Oil Exploitation in South Sudan using Multi-Temporal Landsat Imagery,
PFG(2016), No. 4, 2016, pp. 211-223.
DOI Link 1703

Zhang, B., Li, X., Perrie, W., Garcia-Pineda, O.,
Compact Polarimetric Synthetic Aperture Radar for Marine Oil Platform and Slick Detection,
GeoRS(55), No. 3, March 2017, pp. 1407-1423.
Image reconstruction BibRef

Chenault, D.B.[David B.], Vaden, J.P.[Justin P.], Mitchell, D.A.[Douglas A.], Demicco, E.D.[Erik D.],
New IR polarimeter for improved detection of oil on water,
SPIE(Newsroom), January 18, 2017
DOI Link 1703
Results from a series of tests in a large saltwater tank demonstrate that IR polarimetric images provide much better contrast between oil and water than conventional visible and thermal IR measurements. BibRef

Lupidi, A.[Alberto], Staglianò, D.[Daniele], Martorella, M.[Marco], Berizzi, F.[Fabrizio],
Fast Detection of Oil Spills and Ships Using SAR Images,
RS(9), No. 3, 2017, pp. xx-yy.
DOI Link 1704

Angelliaume, S., Minchew, B., Chataing, S., Martineau, P., Miegebielle, V.,
Multifrequency Radar Imagery and Characterization of Hazardous and Noxious Substances at Sea,
GeoRS(55), No. 5, May 2017, pp. 3051-3066.
oceanography, remote sensing by radar, seawater, water pollution, AD 2015 05, HNS monitoring, Mediterranean Sea, airborne radar sensor, collecting evidence, environmental chemical spills, hazardous-noxious substance, illegal maritime pollution, maritime traffic, multifrequency radar imagery, multifrequency radar system, normalized polarization difference parameter, noxious liquid substance, ocean surface, oil spills, radar remote sensing, sea surface, seawater, Chemicals, Monitoring, Oils, Radar imaging, Spaceborne radar, Synthetic aperture radar, Chemical, hazardous and noxious substance (HNS), multifrequency, normalized polarization difference (NPD), ocean, oil, oil and water mixing index, polarimetry, pollution, sea surface, slick, spill, synthetic, aperture, radar, (SAR) BibRef

Moreira Scafutto, R.D.[Rebecca Del'Papa], de Souza Filho, C.R.[Carlos Roberto], de Oliveira, W.J.[Wilson José],
Hyperspectral remote sensing detection of petroleum hydrocarbons in mixtures with mineral substrates: Implications for onshore exploration and monitoring,
PandRS(128), No. 1, 2017, pp. 146-157.
Elsevier DOI 1706
Hydrocarbons BibRef

Garcia-Pineda, O.[Oscar], Holmes, J.[Jamie], Rissing, M.[Matt], Jones, R.[Russell], Wobus, C.[Cameron], Svejkovsky, J.[Jan], Hess, M.[Mark],
Detection of Oil near Shorelines during the Deepwater Horizon Oil Spill Using Synthetic Aperture Radar (SAR),
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706

Mo, Y.[Yu], Kearney, M.S.[Michael S.], Riter, J.C.A.[J. C. Alexis],
Post-Deepwater Horizon Oil Spill Monitoring of Louisiana Salt Marshes Using Landsat Imagery,
RS(9), No. 6, 2017, pp. xx-yy.
DOI Link 1706

Espeseth, M.M., Skrunes, S., Jones, C.E., Brekke, C., Holt, B., Doulgeris, A.P.,
Analysis of Evolving Oil Spills in Full-Polarimetric and Hybrid-Polarity SAR,
GeoRS(55), No. 7, July 2017, pp. 4190-4210.
Feature extraction, Oils, Polarization, Sea surface, Synthetic aperture radar, Time series analysis, Hybrid polarity (HP), NORSE2015, oil spill observation, synthetic aperture radar (SAR), time series, uninhabited aerial vehicle synthetic aperture radar, (UAVSAR) BibRef

Firoozy, N., Neusitzer, T., Desmond, D.S., Tiede, T., Lemes, M.J.L., Landy, J., Mojabi, P., Rysgaard, S., Stern, G., Barber, D.G.,
An Electromagnetic Detection Case Study on Crude Oil Injection in a Young Sea Ice Environment,
GeoRS(55), No. 8, August 2017, pp. 4465-4475.
Laser radar, Oils, Rough surfaces, Sea ice, Sea surface, Surface roughness, Surface topography, Arctic, crude oil, electromagnetic, scattering, remote, sensing BibRef

Firoozy, N., Neusitzer, T., Chirkova, D., Desmond, D.S., Lemes, M.J.L., Landy, J., Mojabi, P., Rysgaard, S., Stern, G., Barber, D.G.,
A Controlled Experiment on Oil Release Beneath Thin Sea Ice and Its Electromagnetic Detection,
GeoRS(56), No. 8, August 2018, pp. 4406-4419.
chromatography, crude oil, ground penetrating radar, oceanographic techniques, remote sensing by radar, sea ice, remote sensing BibRef

Song, D.M.[Dong-Mei], Ding, Y.X.[Ya-Xiong], Li, X.F.[Xiao-Feng], Zhang, B.[Biao], Xu, M.Y.[Ming-Yu],
Ocean Oil Spill Classification with RADARSAT-2 SAR Based on an Optimized Wavelet Neural Network,
RS(9), No. 8, 2017, pp. xx-yy.
DOI Link 1708

Chen, T., Lu, S.,
Subcategory-Aware Feature Selection and SVM Optimization for Automatic Aerial Image-Based Oil Spill Inspection,
GeoRS(55), No. 9, September 2017, pp. 5264-5273.
coastal ecosystem, marine ecosystem, synthetic aperture radar BibRef

Li, L., Le Dimet, F.X., Ma, J., Vidard, A.,
A Level-Set-Based Image Assimilation Method: Potential Applications for Predicting the Movement of Oil Spills,
GeoRS(55), No. 11, November 2017, pp. 6330-6343.
Mathematical model, Numerical models, Oceans, Oils, Pollution measurement, Predictive models, Image assimilation, level-set method, oil spills. BibRef

Cao, Y.F.[Yong-Feng], Xu, L.L.[Lin-Lin], Clausi, D.[David],
Exploring the Potential of Active Learning for Automatic Identification of Marine Oil Spills Using 10-Year (2004-2013) RADARSAT Data,
RS(9), No. 10, 2017, pp. xx-yy.
DOI Link 1711

Xu, L.L.[Lin-Lin], Shafiee, M.J.[M. Javad], Wong, A.[Alexander], Li, F.[Fan], Wang, L.[Lei], Clausi, D.[David],
Oil spill candidate detection from SAR imagery using a thresholding-guided stochastic fully-connected conditional random field model,
Nickel; Noise; Radar; Speckle BibRef

de Araújo Carvalho, G.[Gustavo], Minnett, P.J.[Peter J.], de Miranda, F.P.[Fernando Pellon], Landau, L.[Luiz], Paes, E.T.[Eduardo Tavares],
Exploratory Data Analysis of Synthetic Aperture Radar (SAR) Measurements to Distinguish the Sea Surface Expressions of Naturally-Occurring Oil Seeps from Human-Related Oil Spills in Campeche Bay (Gulf of Mexico),
IJGI(6), No. 12, 2017, pp. xx-yy.
DOI Link 1801

Neusitzer, T.D., Firoozy, N., Tiede, T.M., Desmond, D.S., Lemes, M.J.L., Stern, G.A., Rysgaard, S., Mojabi, P., Barber, D.G.,
Examining the Impact of a Crude Oil Spill on the Permittivity Profile and Normalized Radar Cross Section of Young Sea Ice,
GeoRS(56), No. 2, February 2018, pp. 921-936.
crude oil, marine pollution, oceanographic techniques, oil pollution, permittivity, radar cross-sections, young sea ice BibRef

Romanov, A.N.,
Dielectric and Radio-Emission Properties of Oil-Polluted Soils,
GeoRS(56), No. 3, March 2018, pp. 1767-1773.
moisture, oil pollution, sand, soil, soil pollution, water, bound water, dielectric properties, dry sand, humidity, microwave emission, soil BibRef

Fingas, M.[Merv],
The Challenges of Remotely Measuring Oil Slick Thickness,
RS(10), No. 2, 2018, pp. xx-yy.
DOI Link 1804

Zhao, D.[Dong], Cheng, X.W.[Xin-Wen], Zhang, H.P.[Hong-Ping], Niu, Y.F.[Yan-Fei], Qi, Y.Y.[Yang-Yang], Zhang, H.T.[Hai-Tao],
Evaluation of the Ability of Spectral Indices of Hydrocarbons and Seawater for Identifying Oil Slicks Utilizing Hyperspectral Images,
RS(10), No. 3, 2018, pp. xx-yy.
DOI Link 1804

Najoui, Z., Riazanoff, S., Deffontaines, B., Xavier, J.P.,
A Statistical Approach to Preprocess and Enhance C-Band SAR Images in Order to Detect Automatically Marine Oil Slicks,
GeoRS(56), No. 5, May 2018, pp. 2554-2564.
Ocean temperature, Oils, Radar imaging, Sea surface, Surface waves, Synthetic aperture radar, C-band MODel (CMOD), Caspian Sea, synthetic aperture radar (SAR) BibRef

Angelliaume, S., Dubois-Fernandez, P.C., Jones, C.E., Holt, B., Minchew, B., Amri, E., Miegebielle, V.,
SAR Imagery for Detecting Sea Surface Slicks: Performance Assessment of Polarization-Dependent Parameters,
GeoRS(56), No. 8, August 2018, pp. 4237-4257.
marine pollution, oceanographic techniques, oil pollution, radar imaging, radar polarimetry, remote sensing, spill BibRef

Ermakov, S.A.[Stanislav A.], Sergievskaya, I.A.[Irina A.], da Silva, J.C.B.[José C.B.], Kapustin, I.A.[Ivan A.], Shomina, O.V.[Olga V.], Kupaev, A.V.[Alexander V.], Molkov, A.A.[Alexander A.],
Remote Sensing of Organic Films on the Water Surface Using Dual Co-Polarized Ship-Based X-/C-/S-Band Radar and TerraSAR-X,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808

Li, G.N.[Guan-Nan], Li, Y.[Ying], Liu, B.X.[Bing-Xin], Hou, Y.C.[Yong-Chao], Fan, J.C.[Jian-Chao],
Analysis of Scattering Properties of Continuous Slow-Release Slicks on the Sea Surface Based on Polarimetric Synthetic Aperture Radar,
IJGI(7), No. 7, 2018, pp. xx-yy.
DOI Link 1808

Angelliaume, S.[Sébastien], Boisot, O.[Olivier], Guérin, C.A.[Charles-Antoine],
Dual-Polarized L-Band SAR Imagery for Temporal Monitoring of Marine Oil Slick Concentration,
RS(10), No. 7, 2018, pp. xx-yy.
DOI Link 1808

Boisot, O.[Olivier], Angelliaume, S.[Sébastien], Guérin, C.A.[Charles-Antoine],
Marine Oil Slicks Quantification From L-band Dual-Polarization SAR Imagery,
GeoRS(57), No. 4, April 2019, pp. 2187-2197.
geophysical image processing, marine pollution, oceanographic regions, oil pollution, radar polarimetry, volume fraction BibRef

Nieto-Hidalgo, M., Gallego, A.J.[Antonio-Javier], Gil, P.[Pablo], Pertusa, A.[Antonio],
Two-Stage Convolutional Neural Network for Ship and Spill Detection Using SLAR Images,
GeoRS(56), No. 9, September 2018, pp. 5217-5230.
Oils, Marine vehicles, Synthetic aperture radar, Sensors, Aircraft, Task analysis, Feature extraction, Neural networks, supervised learning BibRef

Gallego, A.J.[Antonio-Javier], Gil, P.[Pablo], Pertusa, A.[Antonio], Fisher, R.B.[Robert B.],
Semantic Segmentation of SLAR Imagery with Convolutional LSTM Selectional AutoEncoders,
RS(11), No. 12, 2019, pp. xx-yy.
DOI Link 1907

Yu, X., Zhang, H., Luo, C., Qi, H., Ren, P.,
Oil Spill Segmentation via Adversarial f-Divergence Learning,
GeoRS(56), No. 9, September 2018, pp. 4973-4988.
Oils, Image segmentation, Synthetic aperture radar, Minimization, Training, Generators, Manuals, Adversarial learning, synthetic aperture radar (SAR) image processing BibRef

Gürtler, S., Filho, C.R.S.[C.R. Souza], Sanches, I.D., Alves, M.N., Oliveira, W.J.,
Determination of changes in leaf and canopy spectra of plants grown in soils contaminated with petroleum hydrocarbons,
PandRS(146), 2018, pp. 272-288.
Elsevier DOI 1812
Visible and infrared reflectance spectroscopy, Contamination, Liquid hydrocarbons, Vegetation stress, Hyperspectral BibRef

Salberg, A., Larsen, S.Ø.,
Classification of Ocean Surface Slicks in Simulated Hybrid-Polarimetric SAR Data,
GeoRS(56), No. 12, December 2018, pp. 7062-7073.
Oils, Synthetic aperture radar, Sea surface, Polarimetry, Surface waves, Scattering, Machine learning, object recognition, polarimetric synthetic aperture radar (SAR) BibRef

Shi, J.[Jing], Jiao, J.N.[Jun-Nan], Lu, Y.C.[Ying-Cheng], Zhang, M.W.[Min-Wei], Mao, Z.H.[Zhi-Hua], Liu, Y.X.[Yong-Xue],
Determining spectral groups to distinguish oil emulsions from Sargassum over the Gulf of Mexico using an airborne imaging spectrometer,
PandRS(146), 2018, pp. 251-259.
Elsevier DOI 1812
Marine spilled oils, Spectral features, Imaging spectrometer, Hyperspectral remote sensing BibRef

Zheng, H.L.[Hong-Lei], Zhang, Y.M.[Yan-Min], Khenchaf, A.[Ali], Wang, Y.H.[Yun-Hua], Ghanmi, H.[Helmi], Zhao, C.F.[Chao-Fang],
Investigation of EM Backscattering from Slick-Free and Slick-Covered Sea Surfaces Using the SSA-2 and SAR Images,
RS(10), No. 12, 2018, pp. xx-yy.
DOI Link 1901

Tong, S.[Shengwu], Liu, X.[Xiuguo], Chen, Q.[Qihao], Zhang, Z.J.[Zheng-Jia], Xie, G.Q.[Guang-Qi],
Multi-Feature Based Ocean Oil Spill Detection for Polarimetric SAR Data Using Random Forest and the Self-Similarity Parameter,
RS(11), No. 4, 2019, pp. xx-yy.
DOI Link 1903

Pelta, R.[Ran], Ben-Dor, E.[Eyal],
An Exploratory Study on the Effect of Petroleum Hydrocarbon on Soils Using Hyperspectral Longwave Infrared Imagery,
RS(11), No. 5, 2019, pp. xx-yy.
DOI Link 1903

Buono, A., Nunziata, F., de Macedo, C.R., Velotto, D., Migliaccio, M.,
A Sensitivity Analysis of the Standard Deviation of the Copolarized Phase Difference for Sea Oil Slick Observation,
GeoRS(57), No. 4, April 2019, pp. 2022-2030.
geophysical image processing, marine pollution, oceanographic regions, oil pollution, remote sensing by radar, synthetic aperture radar BibRef

Liu, P.[Peng], Li, Y.[Ying], Liu, B.X.[Bing-Xin], Chen, P.[Peng], Xu, J.[Jin],
Semi-Automatic Oil Spill Detection on X-Band Marine Radar Images Using Texture Analysis, Machine Learning, and Adaptive Thresholding,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904

Li, H.[Haiyan], Perrie, W.[William], Wu, J.[Jin],
Retrieval of Oil-Water Mixture Ratio at Ocean Surface Using Compact Polarimetry Synthetic Aperture Radar,
RS(11), No. 7, 2019, pp. xx-yy.
DOI Link 1904

Sun, S., Hu, C.,
The Challenges of Interpreting Oil-Water Spatial and Spectral Contrasts for the Estimation of Oil Thickness: Examples From Satellite and Airborne Measurements of the Deepwater Horizon Oil Spill,
GeoRS(57), No. 5, May 2019, pp. 2643-2658.
geophysical image processing, marine pollution, oceanographic techniques, oil pollution, remote sensing, resolution BibRef

Zhu, X.Y.[Xue-Yuan], Li, Y.[Ying], Zhang, Q.A.[Qi-Ang], Liu, B.X.[Bing-Xin],
Oil Film Classification Using Deep Learning-Based Hyperspectral Remote Sensing Technology,
IJGI(8), No. 4, 2019, pp. xx-yy.
DOI Link 1905

Liu, B.X.[Bing-Xin], Li, Y.[Ying], Li, G.N.[Guan-Nan], Liu, A.L.[An-Ling],
A Spectral Feature Based Convolutional Neural Network for Classification of Sea Surface Oil Spill,
IJGI(8), No. 4, 2019, pp. xx-yy.
DOI Link 1905

Jia, H.[Heming], Xing, Z.[Zhikai], Song, W.L.[Wen-Long],
Three Dimensional Pulse Coupled Neural Network Based on Hybrid Optimization Algorithm for Oil Pollution Image Segmentation,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905

Zhan, S.Y.[Shu-Yue], Wang, C.[Chao], Liu, S.C.[Shu-Chang], Xia, K.[Kaibo], Huang, H.[Hui], Li, X.R.[Xiao-Run], Liu, C.C.[Cai-Cai], Xu, R.[Ren],
Floating Xylene Spill Segmentation from Ultraviolet Images via Target Enhancement,
RS(11), No. 9, 2019, pp. xx-yy.
DOI Link 1905

Xu, J.[Jin], Wang, H.X.[Hai-Xia], Cui, C.[Can], Liu, P.[Peng], Zhao, Y.[Yang], Li, B.[Bo],
Oil Spill Segmentation in Ship-Borne Radar Images with an Improved Active Contour Model,
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908

de Araújo Carvalho, G.[Gustavo ], Minnett, P.J.[Peter J.], Paes, E.T.[Eduardo T.], de Miranda, F.P.[Fernando P.], Landau, L.[Luiz],
Oil-Slick Category Discrimination (Seeps vs. Spills): A Linear Discriminant Analysis Using RADARSAT-2 Backscatter Coefficients (s°, ß°, and ?°) in Campeche Bay (Gulf of Mexico),
RS(11), No. 14, 2019, pp. xx-yy.
DOI Link 1908

Krestenitis, M.[Marios], Orfanidis, G.[Georgios], Ioannidis, K.[Konstantinos], Avgerinakis, K.[Konstantinos], Vrochidis, S.[Stefanos], Kompatsiaris, I.[Ioannis],
Oil Spill Identification from Satellite Images Using Deep Neural Networks,
RS(11), No. 15, 2019, pp. xx-yy.
DOI Link 1908
Early Identification of Oil Spills in Satellite Images Using Deep CNNs,
Springer DOI 1901
Earlier: A2, A3, A4, A5, A6, Only:
A Deep Neural Network for Oil Spill Semantic Segmentation in Sar Images,
Oils, Feature extraction, Synthetic aperture radar, Image segmentation, Satellites, Pollution, Convolution, Convolutional Neural Networks BibRef

Lassalle, G.[Guillaume], Elger, A.[Arnaud], Credoz, A.[Anthony], Hédacq, R.[Rémy], Bertoni, G.[Georges], Dubucq, D.[Dominique], Fabre, S.[Sophie],
Toward Quantifying Oil Contamination in Vegetated Areas Using Very High Spatial and Spectral Resolution Imagery,
RS(11), No. 19, 2019, pp. xx-yy.
DOI Link 1910

Onyia, N.N.[Nkeiruka Nneti], Balzter, H.[Heiko], Berrio, J.C.[Juan Carlos],
Spectral Diversity Metrics for Detecting Oil Pollution Effects on Biodiversity in the Niger Delta,
RS(11), No. 22, 2019, pp. xx-yy.
DOI Link 1911

Satriano, V.[Valeria], Ciancia, E.[Emanuele], Lacava, T.[Teodosio], Pergola, N.[Nicola], Tramutoli, V.[Valerio],
Improving the RST-OIL Algorithm for Oil Spill Detection under Severe Sun Glint Conditions,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912

Skrunes, S.[Stine], Johansson, A.M.[A. Malin], Brekke, C.[Camilla],
Synthetic Aperture Radar Remote Sensing of Operational Platform Produced Water Releases,
RS(11), No. 23, 2019, pp. xx-yy.
DOI Link 1912

Park, S.H.[Sung-Hwan], Jung, H.S.[Hyung-Sup], Lee, M.J.[Moung-Jin],
Oil Spill Mapping from Kompsat-2 High-Resolution Image Using Directional Median Filtering and Artificial Neural Network,
RS(12), No. 2, 2020, pp. xx-yy.
DOI Link 2001

Yin, J.J.[Jun-Jun], Yang, J.[Jian], Zhou, L.J.[Liang-Jiang], Xu, L.Y.[Li-Ying],
Oil Spill Discrimination by Using General Compact Polarimetric SAR Features,
RS(12), No. 3, 2020, pp. xx-yy.
DOI Link 2002

Zeng, K.[Kan], Wang, Y.[Yixiao],
A Deep Convolutional Neural Network for Oil Spill Detection from Spaceborne SAR Images,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003

Zhang, J.[Jin], Feng, H.[Hao], Luo, Q.L.[Qing-Li], Li, Y.[Yu], Wei, J.[Jujie], Li, J.[Jian],
Oil Spill Detection in Quad-Polarimetric SAR Images Using an Advanced Convolutional Neural Network Based on SuperPixel Model,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link 2003

Zhou, Y., Lu, Y., Shen, Y., Ding, J., Zhang, M., Mao, Z.,
Polarized Remote Inversion of the Refractive Index of Marine Spilled Oil From PARASOL Images Under Sunglint,
GeoRS(58), No. 4, April 2020, pp. 2710-2719.
Oils, Optical polarization, Optical imaging, Optical sensors, Remote sensing, Rough surfaces, Surface roughness, sunglint BibRef

Ivonin, D.[Dmitry], Brekke, C.[Camilla], Skrunes, S.[Stine], Ivanov, A.[Andrei], Kozhelupova, N.[Nataliya],
Mineral Oil Slicks Identification Using Dual Co-polarized Radarsat-2 and TerraSAR-X SAR Imagery,
RS(12), No. 7, 2020, pp. xx-yy.
DOI Link 2004

El-Magd, I.A.[Islam Abou], Zakzouk, M.[Mohamed], Abdulaziz, A.M.[Abdulaziz M.], Ali, E.M.[Elham M.],
The Potentiality of Operational Mapping of Oil Pollution in the Mediterranean Sea near the Entrance of the Suez Canal Using Sentinel-1 SAR Data,
RS(12), No. 8, 2020, pp. xx-yy.
DOI Link 2004

Espeseth, M.M., Brekke, C., Jones, C.E., Holt, B., Freeman, A.,
The Impact of System Noise in Polarimetric SAR Imagery on Oil Spill Observations,
GeoRS(58), No. 6, June 2020, pp. 4194-4214.
Additive noise, multiplicative noise, oil spill, Radarsat-2 (RS-2), signal-to-noise ratio (SNR), synthetic aperture radar (SAR), Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) BibRef

Quigley, C., Brekke, C., Eltoft, T.,
Retrieval of Marine Surface Slick Dielectric Properties From Radarsat-2 Data via a Polarimetric Two-Scale Model,
GeoRS(58), No. 7, July 2020, pp. 5162-5178.
Oils, Sea surface, Synthetic aperture radar, Dielectrics, Scattering, Rough surfaces, Dielectric properties, look-alike, oil spill, synthetic aperture radar (SAR) BibRef

de Araújo Carvalho, G.[Gustavo], Minnett, P.J.[Peter J.], Ebecken, N.F.F.[Nelson F. F.], Landau, L.[Luiz],
Classification of Oil Slicks and Look-Alike Slicks: A Linear Discriminant Analysis of Microwave, Infrared, and Optical Satellite Measurements,
RS(12), No. 13, 2020, pp. xx-yy.
DOI Link 2007

Bianchi, F.M.[Filippo Maria], Espeseth, M.M.[Martine M.], Borch, N.[Njål],
Large-Scale Detection and Categorization of Oil Spills from SAR Images with Deep Learning,
RS(12), No. 14, 2020, pp. xx-yy.
DOI Link 2007

Temitope Yekeen, S.[Shamsudeen], Balogun, A.L.[Abdul-Lateef], Wan Yusof, K.B.[Khamaruzaman B.],
A novel deep learning instance segmentation model for automated marine oil spill detection,
PandRS(167), 2020, pp. 190-200.
Elsevier DOI 2008
Earlier: A1, A2, Only:
Automated Marine Oil Spill Detection Using Deep Learning Instance Segmentation Model,
DOI Link 2012
Oil spill, Deep learning, Detection, Mask R-CNN, Instance segmentation, SAR BibRef

Al-Ruzouq, R.[Rami], Gibril, M.B.A.[Mohamed Barakat A.], Shanableh, A.[Abdallah], Kais, A.[Abubakir], Hamed, O.[Osman], Al-Mansoori, S.[Saeed], Khalil, M.A.[Mohamad Ali],
Sensors, Features, and Machine Learning for Oil Spill Detection and Monitoring: A Review,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010

Yekeen, S.T.[Shamsudeen Temitope], Balogun, A.L.[Abdul-Lateef],
Advances in Remote Sensing Technology, Machine Learning and Deep Learning for Marine Oil Spill Detection, Prediction and Vulnerability Assessment,
RS(12), No. 20, 2020, pp. xx-yy.
DOI Link 2010

Vasconcelos, R.N.[Rodrigo N.], Lima, A.T.C.[André T. Cunha], Lentini, C.A.D.[Carlos A. D.], Miranda, G.V.[Garcia V.], Mendonça, L.F.[Luís F.], Silva, M.A.[Marcus A.], Cambuí, E.C.B.[Elaine C. B.], Lopes, J.M.[José M.], Porsani, M.J.[Milton J.],
Oil Spill Detection and Mapping: A 50-Year Bibliometric Analysis,
RS(12), No. 21, 2020, pp. xx-yy.
DOI Link 2011

de Kerf, T.[Thomas], Gladines, J.[Jona], Sels, S.[Seppe], Vanlanduit, S.[Steve],
Oil Spill Detection Using Machine Learning and Infrared Images,
RS(12), No. 24, 2020, pp. xx-yy.
DOI Link 2012

Zheng, H., Zhang, J., Zhang, Y., Khenchaf, A., Wang, Y.,
Theoretical Study on Microwave Scattering Mechanisms of Sea Surfaces Covered With and Without Oil Film for Incidence Angle Smaller Than 30°,
GeoRS(59), No. 1, January 2021, pp. 37-46.
Scattering, Sea surface, Oils, Damping, Surface waves, Surface cleaning, Surface contamination, small-slope approximation (SSA) BibRef

Yang, Y., Chen, K.S., Yang, X., Li, Z.L., Zeng, J.,
Depolarized Scattering of Rough Surface With Dielectric Inhomogeneity and Spatial Anisotropy,
GeoRS(59), No. 1, January 2021, pp. 47-59.
Scattering, Rough surfaces, Surface roughness, Dielectrics, Anisotropic magnetoresistance, Nonhomogeneous media, Sea surface, rough surface BibRef

Zhang, Y., Zheng, H., Wang, Y., Wang, R., Guo, L.,
Investigation on THz EM Wave Scattering From Oil-Covered Sea Surface: Exploration for an Approach to Probe the Thickness of Oil Film,
GeoRS(59), No. 3, March 2021, pp. 1827-1835.
Oils, Scattering, Sea surface, Dielectric constant, Surface waves, Microwave theory and techniques, Radar, Oil film thickness, terahertz (THz) electromagnetic (EM) scattering BibRef

Chatziantoniou, A.[Andromachi], Karagaitanakis, A.[Alexandros], Bakopoulos, V.[Vasileios], Papandroulakis, N.[Nikos], Topouzelis, K.[Konstantinos],
Detection of Biogenic Oil Films near Aquaculture Sites Using Sentinel-1 and Sentinel-2 Satellite Images,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105

Li, G.[Guannan], Li, Y.[Ying], Hou, Y.[Yongchao], Wang, X.[Xiang], Wang, L.[Lin],
Marine Oil Slick Detection Using Improved Polarimetric Feature Parameters Based on Polarimetric Synthetic Aperture Radar Data,
RS(13), No. 9, 2021, pp. xx-yy.
DOI Link 2105

Baszanowska, E.[Emilia], Otremba, Z.[Zbigniew], Piskozub, J.[Jacek],
Modelling the Visibility of Baltic-Type Crude Oil Emulsion Dispersed in the Southern Baltic Sea,
RS(13), No. 10, 2021, pp. xx-yy.
DOI Link 2105

Otremba, Z.[Zbigniew], Piskozub, J.[Jacek],
Modelling the Spectral Index to Detect a Baltic-Type Crude Oil Emulsion Dispersed in the Southern Baltic Sea,
RS(13), No. 19, 2021, pp. xx-yy.
DOI Link 2110

Conceição, M.R.A.[Marcos Reinan Assis], de Mendonça, L.F.F.[Luis Felipe Ferreira], Lentini, C.A.D.[Carlos Alessandre Domingos], da Cunha Lima, A.T.[André Telles], Lopes, J.M.[José Marques], de Vasconcelos, R.N.[Rodrigo Nogueira], Gouveia, M.B.[Mainara Biazati], Porsani, M.J.[Milton José],
SAR Oil Spill Detection System through Random Forest Classifiers,
RS(13), No. 11, 2021, pp. xx-yy.
DOI Link 2106

Li, Y.Q.[Yong-Qing], Lyu, X.R.[Xin-Rong], Frery, A.C.[Alejandro C.], Ren, P.[Peng],
Oil Spill Detection with Multiscale Conditional Adversarial Networks with Small-Data Training,
RS(13), No. 12, 2021, pp. xx-yy.
DOI Link 2106

Zheng, H.L.[Hong-Lei], Zhang, J.[Jie], Khenchaf, A.[Ali], Li, X.M.[Xiao-Ming],
Study on Non-Bragg Microwave Backscattering from Sea Surface Covered with and without Oil Film at Moderate Incidence Angles,
RS(13), No. 13, 2021, pp. xx-yy.
DOI Link 2107

El-Magd, I.A.[Islam Abou], Zakzouk, M.[Mohamed], Ali, E.M.[Elham M.], Abdulaziz, A.M.[Abdulaziz M.],
An Open Source Approach for Near-Real Time Mapping of Oil Spills along the Mediterranean Coast of Egypt,
RS(13), No. 14, 2021, pp. xx-yy.
DOI Link 2107

Almulihi, A.[Ahmed], Alharithi, F.[Fahd], Bourouis, S.[Sami], Alroobaea, R.[Roobaea], Pawar, Y.[Yogesh], Bouguila, N.[Nizar],
Oil Spill Detection in SAR Images Using Online Extended Variational Learning of Dirichlet Process Mixtures of Gamma Distributions,
RS(13), No. 15, 2021, pp. xx-yy.
DOI Link 2108

Baek, W.K.[Won-Kyung], Jung, H.S.[Hyung-Sup],
Performance Comparison of Oil Spill and Ship Classification from X-Band Dual- and Single-Polarized SAR Image Using Support Vector Machine, Random Forest, and Deep Neural Network,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109

Fan, Y.[Yonglei], Rui, X.P.[Xiao-Ping], Zhang, G.[Guangyuan], Yu, T.[Tian], Xu, X.[Xijie], Poslad, S.[Stefan],
Feature Merged Network for Oil Spill Detection Using SAR Images,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link 2109

de Araújo Carvalho, G.[Gustavo], Minnett, P.J.[Peter J.], Ebecken, N.F.F.[Nelson F. F.], Landau, L.[Luiz],
Oil Spills or Look-Alikes? Classification Rank of Surface Ocean Slick Signatures in Satellite Data,
RS(13), No. 17, 2021, pp. xx-yy.
DOI Link 2109

Diana, L.[Lorenzo], Xu, J.[Jia], Fanucci, L.[Luca],
Oil Spill Identification from SAR Images for Low Power Embedded Systems Using CNN,
RS(13), No. 18, 2021, pp. xx-yy.
DOI Link 2109

de Laurentiis, L.[Leonardo], Jones, C.E.[Cathleen E.], Holt, B.[Benjamin], Schiavon, G.[Giovanni], del Frate, F.[Fabio],
Deep Learning for Mineral and Biogenic Oil Slick Classification With Airborne Synthetic Aperture Radar Data,
GeoRS(59), No. 10, October 2021, pp. 8455-8469.
Oils, Minerals, Synthetic aperture radar, Scattering, Sea surface, Backscatter, Sensitivity, Classification, synthetic aperture radar (SAR) BibRef

Krek, E.V.[Elena V.], Krek, A.V.[Alexander V.], Kostianoy, A.G.[Andrey G.],
Chronic Oil Pollution from Vessels and Its Role in Background Pollution in the Southeastern Baltic Sea,
RS(13), No. 21, 2021, pp. xx-yy.
DOI Link 2112

Fifani, G.[Gina], Baudena, A.[Alberto], Fakhri, M.[Milad], Baaklini, G.[Georges], Faugère, Y.[Yannice], Morrow, R.[Rosemary], Mortier, L.[Laurent], d'Ovidio, F.[Francesco],
Drifting Speed of Lagrangian Fronts and Oil Spill Dispersal at the Ocean Surface,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112

de Oliveira Matias, Í.[Ítalo], Genovez, P.C.[Patrícia Carneiro], Torres, S.B.[Sarah Barrón], de Araújo Ponte, F.F.[Francisco Fábio], de Oliveira, A.J.S.[Anderson José Silva], de Miranda, F.P.[Fernando Pellon], Avellino, G.M.[Gil Márcio],
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,
RS(13), No. 22, 2021, pp. xx-yy.
DOI Link 2112

Jiang, Z.C.[Zong-Chen], Zhang, J.[Jie], Ma, Y.[Yi], Mao, X.P.[Xing-Peng],
Hyperspectral Remote Sensing Detection of Marine Oil Spills Using an Adaptive Long-Term Moment Estimation Optimizer,
RS(14), No. 1, 2022, pp. xx-yy.
DOI Link 2201

Mdakane, L.W., Meyer, R.G.V., Sibolla, B.,
Bilge Dump Automatic Alert System In Southern Africa Oceans,
DOI Link 2012

Umar, H.A., Abdul Khanan, M.F., Ahmad, A., Sani, M.J., Abd Rahman, M.Z., Abdul Rahman, A.,
Spatial Database Development for Oil Spills Pollution Affecting Water Quality System in Niger Delta,
DOI Link 1912

Althawadi, J.J.A., Hashim, M.,
An Approach of Vicarious Calibration of Sentinel-2 Satellite Multispectral Image Based On Spectral Library for Mapping Oil Spills,
DOI Link 1912

Dubucq, D.[Dominique], Sicot, G.[Guillaume], Lennon, M.[Marc], Miegebielle, V.[Véronique],
Detection And Discrimination Of The Thick Oil Patches On The Sea Surface,
ISPRS16(B8: 417-421).
DOI Link 1610

Harahsheh, H.A.,
Oil Spill Detection And Monitoring Of Abu Dhabi Coastal Zone Using Kompsat-5 Sar Imagery,
ISPRS16(B8: 1115-1121).
DOI Link 1610

Schvartzman, I., Havivi, S., Maman, S., Rotman, S.R., Blumberg, D.G.,
Large Oil Spill Classification Using Sar Images Based On Spatial Histogram,
ISPRS16(B8: 1183-1186).
DOI Link 1610

Sicot, G., Lennon, M., Miegebielle, V., Dubucq, D.,
Estimation of the Thickness and Emulsion Rate of Oil Spilled at Sea Using Hyperspectral Remote Sensing Imagery in the SWIR Domain,
DOI Link 1602

Wang, Z.H.[Zhi-Hua], Zhong, H.Y.[Hui-Ying], Li, Y.Q.[Yi-Qiang], Zhu-Ge, X.L.[Xiang-Long],
The Application of Signal Collection and Disposal Technology in Measuring the Front of Oil and Water with Polymer Flooding,

Martinis, S.,
Automatic oil spill detection in TerraSAR-X data using multi-contextual Markov modeling on irregular graphs,
Markov processes BibRef

Osmanoglu, B., Ozkan, C., Sunar, F.,
Comparison of Semi-Automatic and Automatic Slick Detection Algorithms For Jiyeh Power Station Oil Spill, Lebanon,
DOI Link 1402

Matkan, A.A., Hajeb, M., Azarakhsh, Z.,
Oil spill detection from SAR image using SVM based classification,
HTML Version. 1311

Sayedain, S.A., Valadan Zouj, M.J., Maghsoudi, Y.,
Exploration of Oil Seepages Using Target Detection Algorithms in Hyperspectral Images,
HTML Version. 1311

Osmanoglu, B., Özkan, C., Sunar, F., Staples, G.,
Automatic Calculation of Oil Slick Area from Multiple SAR Acquisitions for Deepwater Horizon Oil Spill,
DOI Link 1209

Ozkan, C., Osmanoglu, B., Sunar, F., Staples, G., Kalkan, K., Balik Sanli, F.,
Testing The Generalization Efficiency Of Oil Slick Classification Algorithm Using Multiple Sar Data For Deepwater Horizon Oil Spill,
DOI Link 1209

Reis, M.J.C.S.[Manuel J. C. S.], Morais, R.[Raul], Pereira, C.[Carlos], Contente, O.[Olga], Bacelar, M.[Miguel], Soares, S.[Salviano], Valente, A.[António], Baptista, J.[José], Ferreira, P.J.S.G.[Paulo J. S. G.], Bulas-Cruz, J.[José],
A Low-Cost System to Detect Bunches of Grapes in Natural Environment from Color Images,
Springer DOI 1108

Assilzadeh, H.[Hamid], Zhong, Z.N.[Zhi-Nong], Liu, T.[Tim], Gao, Y.[Yang],
Development of an even-driven and scalable oil spill monitoring and management system,
PDF File. 1006

Vazquez-Fernandez, E.[Esteban], Dacal-Nieto, A.[Angel], Martin, F.[Fernando], Formella, A.[Arno], Torres-Guijarro, S.[Soledad], Gonzalez-Jorge, H.[Higinio],
A Computer Vision System for Visual Grape Grading in Wine Cellars,
Springer DOI 0910

Pelizzari, S.[Sónia], Bioucas-Dias, J.M.[José M.],
Bayesian Oil Spill Segmentation of SAR Images Via Graph Cuts,
IbPRIA07(II: 637-644).
Springer DOI 0706

Pourvakhshouri, S.Z., Shattri, B.M., Zelina, Z.I., Noordin, A.,
Decision Support System in Oil Spill Management,
PDF File. 0607

Hese, S., Schmullius, C.,
Object oriented oil spill contamination mapping in West Siberia with Quickbird data,
PDF File. 0607

Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Plastic Litter, Ocean Plastic, Beach Litter .

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