22.5.8 Oil Slicks, Oil Spills, Water Areas

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.[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,
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°, ß°, and ?°) in Campeche Bay (Gulf of Mexico),
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,
MMMod19(I:424-435).
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,
RS(11), No. 19, 2019, pp. xx-yy.
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