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Mercier, G.[Grégoire],
Unsupervised multiscale oil slick segmentation from SAR images using a
vector HMC model,
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Elsevier DOI
0611
Oil slick detection; Multiscale wavelet analysis;
Hidden Markov chain; Unsupervised segmentation
BibRef
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Elsevier DOI
0804
Oil spills; SAR image; Image segmentation;
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BibRef
Marques, R.C.P.,
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IEEE DOI
0903
BibRef
de A. Lopes, D.F.[Darby F.],
Ramalho, G.L.B.[Geraldo L.B.],
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Springer DOI
0608
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0708
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Earlier:
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ICPR06(II: 1066-1069).
IEEE DOI
0609
BibRef
Ramsey, III, E.,
Rangoonwala, A.,
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Oil Detection in a Coastal Marsh with Polarimetric Synthetic Aperture
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BibRef
Velotto, D.,
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Dual-Polarized TerraSAR-X Data for Oil-Spill Observation,
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IEEE DOI
1201
BibRef
Topouzelis, K.[Konstantinos],
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Oil spill feature selection and classification using decision tree
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PandRS(68), No. 1, March 2012, pp. 135-143.
Elsevier DOI
1204
Oil spill; Decision forest; Feature selection; SAR; Classification;
Machine learning
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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.,
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SAR Image Quality Assessment and Indicators for Vessel and Oil Spill
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GeoRS(50), No. 11, November 2012, pp. 4726-4734.
IEEE DOI
1210
BibRef
Nunziata, F.[Ferdinando],
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PandRS(78), No. 1, April 2013, pp. 41-49.
Elsevier DOI
1304
Polarimetry; Synthetic Aperture Radar (SAR); Oil pollution; Degree of
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BibRef
Salberg, A.B.[Arnt-Børre],
Rudjord, O.,
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IEEE DOI
1407
Correlation
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0506
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de Carolis, G.,
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On the Estimation of Thickness of Marine Oil Slicks From
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GeoRS(52), No. 1, January 2014, pp. 559-573.
IEEE DOI
1402
infrared imaging
BibRef
Bandiera, F.,
Masciullo, A.,
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A Bayesian Approach to Oil Slicks Edge Detection Based on SAR Data,
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IEEE DOI
1403
Azimuth
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Skrunes, S.,
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Eltoft, T.,
Characterization of Marine Surface Slicks by Radarsat-2
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1407
marine pollution
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Niclòs, R.[Raquel],
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Thermal-Infrared Spectral and Angular Characterization of Crude Oil
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IEEE DOI
1407
crude oil
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Etellisi, E.A.[Ehab A.],
Deng, Y.M.[Yi-Ming],
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Li, B.[Bin],
Liu, J.Y.[Jing-Yi],
Emissivity Measurements of Foam-Covered Water Surface at L-Band for
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1412
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Taravat, A.,
Latini, D.,
del Frate, F.,
Fully Automatic Dark-Spot Detection From SAR Imagery With the
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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
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DOI Link
1311
Feature extraction.
Oil spill monitoring.
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Pisano, A.[Andrea],
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1502
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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
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IEEE DOI
1506
geophysical image processing
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Yan, J.N.[Ji-Ning],
Wang, L.Z.[Li-Zhe],
Chen, L.J.[La-Jiao],
Zhao, L.J.[Ling-Jun],
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A Dynamic Remote Sensing Data-Driven Approach for Oil Spill
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DOI Link
1507
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Ramsey, E.[Elijah],
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Structural Classification of Marshes with Polarimetric SAR
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1511
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Rapaport, T.[Tal],
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Karnieli, A.[Arnon],
Rachmilevitch, S.[Shimon],
Combining leaf physiology, hyperspectral imaging and partial least
squares-regression (PLS-R) for grapevine water status assessment,
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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
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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
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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.W.[Sheng-Wu],
Liu, X.[Xiuguo],
Chen, Q.H.[Qi-Hao],
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
Xie, T.[Tao],
Ouyang, R.H.[Rui-Hang],
Perrie, W.[Will],
Zhao, L.[Li],
Zhang, X.Y.[Xiao-Yun],
Proof and Application of Discriminating Ocean Oil Spills and Seawater
Based on Polarization Ratio Using Quad-Polarization Synthetic
Aperture Radar,
RS(15), No. 7, 2023, pp. 1855.
DOI Link
2304
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,
IJGI(8), No. 4, 2019, pp. xx-yy.
DOI Link
1905
BibRef
Jia, H.M.[He-Ming],
Xing, Z.K.[Zhi-Kai],
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
BibRef
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
BibRef
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
BibRef
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°,
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RS(11), No. 14, 2019, pp. xx-yy.
DOI Link
1908
BibRef
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
BibRef
Earlier:
Early Identification of Oil Spills in Satellite Images Using Deep CNNs,
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Springer DOI
1901
BibRef
Earlier: A2, A3, A4, A5, A6, Only:
A Deep Neural Network for Oil Spill Semantic Segmentation in Sar
Images,
ICIP18(3773-3777)
IEEE DOI
1809
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,
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DOI Link
1910
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
Zeng, K.[Kan],
Wang, Y.X.[Yi-Xiao],
A Deep Convolutional Neural Network for Oil Spill Detection from
Spaceborne SAR Images,
RS(12), No. 6, 2020, pp. xx-yy.
DOI Link
2003
BibRef
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
BibRef
Zhang, J.[Jin],
Feng, H.[Hao],
Luo, Q.L.[Qing-Li],
Li, Y.[Yu],
Zhang, Y.[Yu],
Li, J.[Jian],
Zeng, Z.[Zhoumo],
Oil Spill Detection with Dual-Polarimetric Sentinel-1 SAR Using
Superpixel-Level Image Stretching and Deep Convolutional Neural
Network,
RS(14), No. 16, 2022, pp. xx-yy.
DOI Link
2208
BibRef
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.
IEEE DOI
2004
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
BibRef
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
BibRef
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.
IEEE DOI
2005
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.
IEEE DOI
2006
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
BibRef
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
BibRef
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
BibRef
Earlier: A1, A2, Only:
Automated Marine Oil Spill Detection Using Deep Learning Instance
Segmentation Model,
ISPRS20(B3:1271-1276).
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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.
IEEE DOI
2012
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.
IEEE DOI
2012
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.
IEEE DOI
2103
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
BibRef
Li, G.N.[Guan-Nan],
Li, Y.[Ying],
Hou, Y.C.[Yong-Chao],
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
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
BibRef
Fan, Y.[Yonglei],
Rui, X.P.[Xiao-Ping],
Zhang, G.Y.[Guang-Yuan],
Yu, T.[Tian],
Xu, X.J.[Xi-Jie],
Poslad, S.[Stefan],
Feature Merged Network for Oil Spill Detection Using SAR Images,
RS(13), No. 16, 2021, pp. xx-yy.
DOI Link
2109
BibRef
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
BibRef
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
BibRef
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.
IEEE DOI
2109
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
BibRef
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
BibRef
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
BibRef
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
BibRef
Otremba, Z.[Zbigniew],
Piskozub, J.[Jacek],
Monte Carlo Radiative Transfer Simulation to Analyze the Spectral
Index for Remote Detection of Oil Dispersed in the Southern Baltic
Sea Seawater Column: The Role of Water Surface State,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link
2201
BibRef
Wang, D.W.[Da-Wei],
Wan, J.H.[Jian-Hua],
Liu, S.W.[Shan-Wei],
Chen, Y.L.[Yan-Long],
Yasir, M.[Muhammad],
Xu, M.M.[Ming-Ming],
Ren, P.[Peng],
BO-DRNet: An Improved Deep Learning Model for Oil Spill Detection by
Polarimetric Features from SAR Images,
RS(14), No. 2, 2022, pp. xx-yy.
DOI Link
2201
BibRef
Mityagina, M.[Marina],
Lavrova, O.[Olga],
Satellite Survey of Offshore Oil Seep Sites in the Caspian Sea,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Yang, J.[Junfang],
Ma, Y.[Yi],
Hu, Y.[Yabin],
Jiang, Z.C.[Zong-Chen],
Zhang, J.[Jie],
Wan, J.H.[Jian-Hua],
Li, Z.W.[Zhong-Wei],
Decision Fusion of Deep Learning and Shallow Learning for Marine Oil
Spill Detection,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Mehdi, S.R.[Syed Raza],
Raza, K.[Kazim],
Huang, H.[Hui],
Naqvi, R.A.[Rizwan Ali],
Ali, A.[Amjad],
Song, H.[Hong],
Combining Deep Learning with Single-Spectrum UV Imaging for Rapid
Detection of HNSs Spills,
RS(14), No. 3, 2022, pp. xx-yy.
DOI Link
2202
hazardous and noxious substances.
BibRef
Pinel, N.[Nicolas],
Bourlier, C.[Christophe],
Sergievskaya, I.[Irina],
Longépé, N.[Nicolas],
Hajduch, G.[Guillaume],
Asymptotic Modeling of Three-Dimensional Radar Backscattering from
Oil Slicks on Sea Surfaces,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Mohr, V.[Veronika],
Gade, M.[Martin],
Marine Oil Pollution in an Area of High Economic Use: Statistical
Analyses of SAR Data from the Western Java Sea,
RS(14), No. 4, 2022, pp. xx-yy.
DOI Link
2202
BibRef
Chen, P.[Peng],
Zhou, H.[Hui],
Li, Y.[Ying],
Liu, B.X.[Bing-Xin],
Liu, P.[Peng],
Oil Spill Identification in Radar Images Using a Soft Attention
Segmentation Model,
RS(14), No. 9, 2022, pp. xx-yy.
DOI Link
2205
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Chapter on Remote Sensing General Issue, Land Use, Land Cover continues in
Plastic Litter, Ocean Plastic, Beach Litter .