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.
IEEE DOI 0903
BibRef

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,
SSPR06(928-936).
Springer DOI 0608
BibRef

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,
ICIAR07(1172-1181).
Springer DOI 0708
BibRef
Earlier:
Using Boosting to Improve Oil Spill Detection in SAR Images,
ICPR06(II: 1066-1069).
IEEE DOI 0609
BibRef

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
BibRef

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.
IEEE DOI 1201
BibRef

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.
IEEE DOI 1210
BibRef

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.
IEEE DOI 1210
BibRef

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.
IEEE DOI 1407
Correlation BibRef

Brekke, C.[Camilla], Solberg, A.H.S.[Anne H.S.],
Feature Extraction for Oil Spill Detection Based on SAR Images,
SCIA05(75-84).
Springer DOI 0506
BibRef

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.
IEEE DOI 1402
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.
IEEE DOI 1403
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.
IEEE DOI 1407
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.
IEEE DOI 1407
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
BibRef

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
BibRef

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.
IEEE DOI 1403
BibRef
Earlier: A1, A3, Only:
Weibull Multiplicative Model and Machine Learning Models for Full-Automatic Dark-Spot Detection from SAR Images,
SMPR13(421-424).
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
BibRef

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.
IEEE DOI 1502
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.
IEEE DOI 1506
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
BibRef

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
BibRef

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

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.
IEEE DOI 1610
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
BibRef

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
BibRef

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
BibRef

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.
IEEE DOI 1701
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
BibRef

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
BibRef

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
BibRef

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.
IEEE DOI 1703
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
BibRef

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.
IEEE DOI 1705
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
BibRef

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
BibRef

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.
IEEE DOI 1706
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.
IEEE DOI 1708
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.
IEEE DOI 1808
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
BibRef

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.
IEEE DOI 1709
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.
IEEE DOI 1711
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
BibRef

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,
EarthObserv15(79-86)
IEEE DOI 1510
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
BibRef

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.
IEEE DOI 1802
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.
IEEE DOI 1804
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
BibRef

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
BibRef

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.
IEEE DOI 1805
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.
IEEE DOI 1808
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
BibRef

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
BibRef

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
BibRef

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.
IEEE DOI 1904
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.
IEEE DOI 1809
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
BibRef

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.
IEEE DOI 1809
Oils, Image segmentation, Synthetic aperture radar, Minimization, Training, Generators, Manuals, Adversarial learning, synthetic aperture radar (SAR) image processing BibRef

Gürtler, S.[Salete], Souza Filho, C.R.[Carlos R.], Sanches, I.D.[Ieda D.], Alves, M.N.[Marcos N.], Oliveira, W.J.[Wilson 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

Gürtler, S.[Salete], Souza Filho, C.R.[Carlos R.], Sanches, I.D.[Ieda D.], Magalhães, L.A.[Lucíola A.], Alves, M.N.[Marcos N.], Oliveira, W.J.[Wilson J.], Quitério, G.C.M.[Giuliana C. M.],
Leaf Spectra Changes of Plants Grown in Soils Pre- and Post-Contaminated with Petroleum Hydrocarbons,
RS(14), No. 14, 2022, pp. xx-yy.
DOI Link 2208
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.
IEEE DOI 1812
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
BibRef

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
BibRef

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
BibRef

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.
IEEE DOI 1904
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
BibRef

Li, H.Y.[Hai-Yan], 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
BibRef

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.
IEEE DOI 1905
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
BibRef

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,
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ICIP18(3773-3777)
IEEE DOI 1809
Oils, Feature extraction, Synthetic aperture radar, Image segmentation, Satellites, Pollution, Convolution, Convolutional Neural Networks BibRef

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Zhou, Y., Lu, Y., Shen, Y., Ding, J., Zhang, M., Mao, Z.,
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IEEE DOI 2004
Oils, Optical polarization, Optical imaging, Optical sensors, Remote sensing, Rough surfaces, Surface roughness, sunglint BibRef

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Espeseth, M.M., Brekke, C., Jones, C.E., Holt, B., Freeman, A.,
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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.,
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IEEE DOI 2006
Oils, Sea surface, Synthetic aperture radar, Dielectrics, Scattering, Rough surfaces, Dielectric properties, look-alike, oil spill, synthetic aperture radar (SAR) BibRef

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IEEE DOI 2012
Scattering, Sea surface, Oils, Damping, Surface waves, Surface cleaning, Surface contamination, small-slope approximation (SSA) BibRef

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IEEE DOI 2012
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IEEE DOI 2103
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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],
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Baek, W.K.[Won-Kyung], Jung, H.S.[Hyung-Sup],
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de Araújo Carvalho, G.[Gustavo], Minnett, P.J.[Peter J.], Ebecken, N.F.F.[Nelson F. F.], Landau, L.[Luiz],
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Diana, L.[Lorenzo], Xu, J.[Jia], Fanucci, L.[Luca],
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IEEE DOI 2109
Oils, Minerals, Synthetic aperture radar, Scattering, Sea surface, Backscatter, Sensitivity, Classification, synthetic aperture radar (SAR) BibRef

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Jiang, Z.C.[Zong-Chen], Zhang, J.[Jie], Ma, Y.[Yi], Mao, X.P.[Xing-Peng],
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Otremba, Z.[Zbigniew], Piskozub, J.[Jacek],
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Wang, D.W.[Da-Wei], Wan, J.H.[Jian-Hua], Liu, S.[Shanwei], Chen, Y.L.[Yan-Long], Yasir, M.[Muhammad], Xu, M.M.[Ming-Ming], Ren, P.[Peng],
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Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Plastic Litter, Ocean Plastic, Beach Litter .


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