SONG Shasha, ZHAO Chaofang, AN Wei, LI Xiaofeng, WANG Chen. Analysis of impacting factors on polarimetric SAR oil spill detection[J]. Acta Oceanologica Sinica, 2018, 37(11): 77-87. doi: 10.1007/s13131-018-1335-9
Citation: SONG Shasha, ZHAO Chaofang, AN Wei, LI Xiaofeng, WANG Chen. Analysis of impacting factors on polarimetric SAR oil spill detection[J]. Acta Oceanologica Sinica, 2018, 37(11): 77-87. doi: 10.1007/s13131-018-1335-9

Analysis of impacting factors on polarimetric SAR oil spill detection

doi: 10.1007/s13131-018-1335-9
  • Received Date: 2017-11-08
  • Polarimetric synthetic aperture radar (SAR) oil spill detection parameters conformity coefficient (μ), Muller matrix parameters (|C|, B0), the eigenvalues of simplified coherency matrix (λnos) and the influence of SAR observing parameters, ocean environment and noise level are investigated. Radarsat-2 data are used to make systematic analysis of polarimetric parameters for different incidences, wind speeds, noise levels and the ocean phenomena (oil slick and look likes). The influence of the SAR observing parameters, the ocean environment and the noise level on the typical polarimetric SAR parameter conformity coefficient has been analyzed. The results indicate that conformity coefficient cannot be simply used for oil spill detection, which represents the image signal to the noise level to some extent. When the signals are below the noise level for the oil slick and the look likes, the conformity coefficients are negative; while the signals above the noise level corresponds to positive conformity coefficients. For dark patches (low wind and biogenic slick) with the signal below the noise, polarization features such as conformity coefficient cannot separate them with oil slick. For the signal above the noise, the oil slick, the look likes (low wind and biogenic slick) and clean sea all have positive conformity coefficients, among which, the oil slick has the smallest conformity coefficient, the look likes the second, and the clean sea the largest value. For polarimetric SAR data oil spill detection, the noise plays a significant role. So the polarimetric SAR data oil spill detection should be carried out on the basis of noise consideration.
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  • Allain S, FerroFamil L, Potier E. 2005. New eigenvalue-based parameters for natural media characterization. In:Proc. Of the POLinSAR 2005 Workshop. Frascati, Italy:European Space Agency, 586
    Buono A, Nunziata F, Migliaccio M, et al. 2016. Polarimetric analysis of compact-polarimetry SAR architectures for sea oil slick observation. IEEE Transactions on Geoscience and Remote Sensing, 54(10):5862-5874, doi: 10.1109/TGRS.2016.2574561
    Cloud S R. 1985. Radar target decomposition theorems. Institute of Electrical Engineering and Electronic Letters, 21(1):22-24
    Dubois-Fernandez P, Freeman A, Truong-Loi M L. 2008. Soil moisture estimation from compact polarimetry-A viable alternative for SMAP. In:Microwave Remote Sensing for Land Hydrology Workshop. Oxnard, CA:Int. of Electr. And Electron. Eng.
    Guissard A. 1994. Mueller and Kennaugh matrices in radar polarimetry. IEEE Transactions on Geoscience and Remote Sensing, 32(3):590-597, doi: 10.1109/36.297977
    Li X F, Li C Y, Yang Z Z, et al. 2013. SAR imaging of ocean surface oil seep trajectories induced by near inertial oscillation. Remote Sensing of Environment, 130:182-187, doi: 10.1016/j.rse.2012.11.019
    Li X, Nunziata F, Garcia O. 2017. Oil spill detection from single-and multipolarization SAR imagery. In:Liang Shunlin, ed. Comprehensive Remote Sensing. Amsterdam:Elsevier, 231-248
    Liu Peng, Li Xiaofeng, Qu J J, et al. 2011. Oil spill detection with fully polarimetric UAVSAR data. Marine Pollution Bulletin, 62(12):2611-2618, doi: 10.1016/j.marpolbul.2011.09.036
    Migliaccio M, Nunziata F, Gambardella A. 2009. On the co-polarized phase difference for oil spill observation. International Journal ofRemote Sensing, 30(6):1587-1602, doi: 10.1080/01431160802520741
    Migliaccio M, Nunziata F, Brown C E, et al. 2012. Polarimetric synthetic aperture radar utilized to track oil spills. EOS Transactions American Geophysical Union, 93(16):161-162, doi: 10.1029/2012EO160001
    Migliaccio M, Nunziata F, Montuori A, et al. 2011. A multifrequency polarimetric SAR processing chain to observe oil fields in the Gulf of Mexico. IEEE Transactions on Geoscience and Remote Sensing, 49(12):4729-4737, doi: 10.1109/TGRS.2011.2158828
    Minchew B, Jones C E, Holt B. 2012. Polarimetric analysis of backscatter from the deepwater horizon oil spill using L-band synthetic aperture radar. IEEE Transactions on Geoscience and Remote Sensing, 50(10):3812-3830, doi: 10.1109/TGRS.2012.2185804
    Nghiem S V, Yueh S H, Kwok R, et al. 1992. Symmetry properties in polarimetric remote sensing. Radio Science, 27(5):693-711, doi: 10.1029/92RS01230
    Nunziata F, Gambardella A, Migliaccio M. 2008. On the Mueller scattering matrix for SAR sea oil slick observation. IEEE Geoscience and Remote Sensing Letters, 5(4):691-695, doi: 10.1109/LGRS.2008.2003127
    Nunziata F, Gambardella A, Migliaccio M. 2012. A unitary Mueller-based view of polarimetric SAR oil slick observation. International Journal of Remote Sensing, 33(20):6403-6425, doi: 10.1080/01431161.2012.687474
    Nunziata F, Gambardella A, Migliaccio M. 2013. On the degree of polarization for SAR sea oil slick observation. ISPRS Journal of Photogrammetry and Remote Sensing, 78:41-49, doi: 10.1016/j.isprsjprs.2012.12.007
    Nunziata F, Migliaccio M, Li Xiaofeng. 2015. Sea oil slick observation using hybrid-polarity SAR architecture. IEEE Journal of Oceanic Engineering, 40(2):426-440, doi: 10.1109/JOE.2014.2329424
    Schuler D L, Lee J S, Hoppel K W. 1993. Polarimetric SAR image signatures of the ocean and Gulf Stream features. IEEE Transactions on Geoscience and Remote Sensing, 31(6):1210-1221, doi: 10.1109/36.317442
    Skrunes S, Brekke C, Eltoft T. 2014. Characterization of marine surface slicks by Radarsat-2 multipolarization features. IEEE Transactions on Geoscience and Remote Sensing, 52(9):5302-5319, doi: 10.1109/TGRS.2013.2287916
    Song Dongmei, Ding Yaxiong, Li Xiaofeng, et al. 2017. Ocean oil spill classification with RADARSAT-2 SAR based on an optimized wavelet neural network. Remote Sensing, 9(8):799, doi: 10.3390/rs9080799
    Ulaby F T, Sarabandi K, Nashashibi A. 1992. Statistical properties of the Mueller matrix of distributed targets. IEE Proceedings F-Radar and Signal Processing, 139(2):136-146, doi: 10.1049/ip-f-2.1992.0017
    Van Zyl J. 1989. Unsupervised classification of scattering behavior using radar polarimetry data. IEEE Transactions on Geoscience and Remote Sensing, 27(1):36-45, doi: 10.1109/36.20273
    Van Zyl J, Papas C, Elachi C. 1987. On the optimum polarizations of incoherently reflected waves. IEEE Transactions on Antennas and Propagation, 35(7):818-825, doi: 10.1109/TAP.1987.1144175
    Velotto D, Migliaccio M, Nunziata F, et al. 2011. Dual-polarized TerraSAR-X data for oil-spill observation. IEEE Transactions on Geoscience and Remote Sensing, 49(12):4751-4762, doi: 10.1109/TGRS.2011.2162960
    Wang Chen, Zhao Chaofang, Zeng Kan, et al. 2015. Analysis of scattering mechanisms over sea oil slicks based on eigenvalues of simplified coherence matrix. Journal of Applied Remote Sensing, 2015:095974
    Zhang Biao, Perrie W, Li Xiaofeng. 2011. Mapping sea surface oil slicks using RADARSAT-2 quad-polarization SAR image. Geophysical Research Letters, 38(10):L10602
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