On the classification of mixed floating pollutants on the Yellow Sea of China by using a quad-polarized SAR image

doi:10.1007/s11707-017-0664-x

Front. Earth Sci.

2018, Vol. 12

Issue (2) : 373-380 https://doi.org/10.1007/s11707-017-0664-x

RESEARCH ARTICLE

On the classification of mixed floating pollutants on the Yellow Sea of China by using a quad-polarized SAR image

Xiaochen WANG^1,², Yun SHAO¹, Wei TIAN^1,³(

), Kun LI¹

¹. Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China
². University of Chinese Academy of Sciences, Beijing 100049, China
³. Joint Institute for Regional Earth System Science and Engineering and Department of Atmospheric and Oceanic Sciences, University of California, Los Angeles, CA 90095, USA

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Abstract

This study explored different methodologies using a C-band RADARSAT-2 quad-polarized Synthetic Aperture Radar (SAR) image located over China’s Yellow Sea to investigate polarization decomposition parameters for identifying mixed floating pollutants from a complex ocean background. It was found that solitary polarization decomposition did not meet the demand for detecting and classifying multiple floating pollutants, even after applying a polarized SAR image. Furthermore, considering that Yamaguchi decomposition is sensitive to vegetation and the algal variety Enteromorpha prolifera, while H/A/alpha decomposition is sensitive to oil spills, a combination of parameters which was deduced from these two decompositions was proposed for marine environmental monitoring of mixed floating sea surface pollutants. A combination of volume scattering, surface scattering, and scattering entropy was the best indicator for classifying mixed floating pollutants from a complex ocean background. The Kappa coefficients for Enteromorpha prolifera and oil spills were 0.7514 and 0.8470, respectively, evidence that the composite polarized parameters based on quad-polarized SAR imagery proposed in this research is an effective monitoring method for complex marine pollution.

Keywords RADARSAT-2 polarization decomposition mixed floating pollutants classification

Corresponding Author(s): Wei TIAN

Just Accepted Date: 17 August 2017 Online First Date: 14 September 2017 Issue Date: 09 May 2018

Cite this article:

Xiaochen WANG,Yun SHAO,Wei TIAN, et al. On the classification of mixed floating pollutants on the Yellow Sea of China by using a quad-polarized SAR image[J]. Front. Earth Sci., 2018, 12(2): 373-380.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-017-0664-x
https://academic.hep.com.cn/fesci/EN/Y2018/V12/I2/373

Tab.1 Specifications of the C-band RADARSAT-2 quad-polarized SAR image

Fig.1 C-band RADARSAT-2 quad-polarized SAR image (R: HH G: HV B: VV). The image time (UTC) was 22:07 on 7/4/2016, the swath was 25 km×25 km, the resolution was 5.2 m×7.6 m, and Zones 1 to 4 correspond to vegetation, Enteromorpha prolifera, oil spills, and open water, respectively.

Fig.2 Different scattering components for vegetation, open water, Enteromorpha prolifera, and oil spills following Freeman-Durden decomposition: (a) surface scattering; (b) double bounce scattering; (c) volume scattering.

Fig.3 Different scattering components for vegetation, open water, Enteromorpha prolifera, and oil spills following Yamaguchi decomposition: (a) surface scattering; (b) double bounce scattering; (c) volume scattering; (d) helix scattering.

Fig.4 Different scattering components for vegetation, open water, Enteromorpha prolifera, and oil spills following H/A/alpha decomposition: (a) scattering entropy; (b) scattering angle; (c) scattering anisotropy.

Tab.2 Polarization parameters for floating pollutants under different polarization decomposition methods

Tab.3 The optimum polarization parameters combination for mixed floating pollutants classification

Fig.5 A pseudo composite image of the optimum polarization parameters.

Tab.4 The typical targets classification accuracy for optimum decomposition parameters

1	Brekke C, Solberg A H S (2005). Oil spill detection by satellite remote sensing. Remote Sens Environ, 95(1): 1–13 https://doi.org/10.1016/j.rse.2004.11.015
2	Cloude S R, Pottier E (1996). A review of target decomposition theorems in radar polarimetry. IEEE Trans Geosci Remote Sens, 34(2): 498–518 https://doi.org/10.1109/36.485127
3	Cloude S R, Pottier E (1997). An entropy based classification scheme for land applications of polarimetric SAR. IEEE Trans Geosci Remote Sens, 35(1): 68–78 https://doi.org/10.1109/36.551935
4	Fletcher R L (1996). The occurrence of “green tides”— A reivew. In: Schramm W, Nienhuis P H, eds. Marine Benthic Vegetation. Springer Berlin Heidelberg, 7–43
5	Freeman A, Durden S L (1998). A three-component scattering model for polarimetric SAR data. IEEE Trans Geosci Remote Sens, 36(3): 963–973 https://doi.org/10.1109/36.673687
6	Hu C M (2009). A novel ocean color index to detect floating algae in the global oceans. Remote Sens Environ, 113(10): 2118–2129 https://doi.org/10.1016/j.rse.2009.05.012
7	Hu C, Li D, Chen C, Ge J, Muller-Karger F E Liu J, Yu F, He M X (2010). On the recurrent Ulva prolifera blooms in the Yellow Sea and East China Sea. J Geophys Res, 115(C5): C05017 https://doi.org/10.1029/2009JC005561
8	Kudryavtsev V N, Chapron B, Myasoedov A G, Collard F, Johannessen J A (2013). On dual co-polarized SAR measurements of the ocean surface. IEEE Geosci Remote Sens Lett, 10(4): 761–765 https://doi.org/10.1109/LGRS.2012.2222341
9	Liu D, Keesing J K, He P, Wang Z, Shi Y, Wang Y (2013). The world’s largest macroalgal bloom in the Yellow Sea, China: formation and implications. Estuarine Coastal & Shelfence, 129: 2–10
10	Liu P, Li X, Qu J J, Wang W, Zhao C, Pichel W (2011). Oil spill detection with fully polarimetric UAVSAR data. Mar Pollut Bull, 62(12): 2611–2618 https://doi.org/10.1016/j.marpolbul.2011.09.036
11	Liu P, Zhao C, Li X, He M, Pichel W (2010). Identification of ocean oil spills in SAR imagery based on fuzzy logic algorithm. Int J Remote Sens, 31(17–18): 4819–4833 https://doi.org/10.1080/01431161.2010.485147
12	Liu X, Li Y, Wang Z, Zhang Q, Cai X (2015). Cruise observation of Ulva prolifera bloom in the southern Yellow Sea, China. Estuar Coast Shelf Sci, 163: 17–22 https://doi.org/10.1016/j.ecss.2014.09.014
13	Migliaccio M, Gambardella A, Nunziata F, Shimada M, Isoguchi O (2009a). The PALSAR polarimetric mode for sea oil slick observation. IEEE Transactions on Geoscience & Remote Sensing, 47(12): 4032–4041
14	Migliaccio M, Gambardella A, Tranfaglia M (2007). SAR polarimetry to observe oil spills. IEEE Trans Geosci Remote Sens, 45(2): 506–511 https://doi.org/10.1109/TGRS.2006.888097
15	Migliaccio M, Nunziata F, Gambardella A (2009b). On the co-polarized phase difference for oil spill observation. Int J Remote Sens, 30(6): 1587–1602 https://doi.org/10.1080/01431160802520741
16	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 Trans Geosci Remote Sens, 50(10): 3812–3830 https://doi.org/10.1109/TGRS.2012.2185804
17	Nunziata F, Migliaccio M, Li X (2014). Sea oil slick observation using hybrid-polarity SAR architecture. IEEE Journal of Oceanic Engineering, 1(2): 426–440
18	Sato A, Yamaguchi Y, Singh G, Park S E (2012). Four-component scattering power decomposition with extended volume scattering model. IEEE Geosci Remote Sens Lett, 9(2): 166–170 https://doi.org/10.1109/LGRS.2011.2162935
19	Shi W, Wang M (2009). Green macroalgae blooms in the Yellow Sea during the spring and summer of 2008. J Geophys Res, D, Atmospheres, 114(C12): C12010 https://doi.org/10.1029/2009JC005513
20	Shirvany R, Chabert M, Tourneret J Y (2012). Ship and oil-spill detection using the degree of polarization in linear and hybrid/compact dual-pol SAR. IEEE Journal of Selected Topics in Applied Earth Observations & Remote Sensing, 5(3): 885–892
21	Shurcliff W (1963). Studies in optics. Journal of Physics and Chemistry of Solids, 24(3): 498–499 https://doi.org/10.1016/0022-3697(63)90212-9
22	Solberg A H S (2012). Remote sensing of ocean oil-spill pollution. Proc IEEE, 100(10): 2931–2945 https://doi.org/10.1109/JPROC.2012.2196250
23	Tian W, Shao Y, Yuan J, Wang S, Liu Y (2010). An Experiment for Oil Spill Recognition Using RADARSAT-2 Image. IEEE International Geoscience & Remote Sensing Symposium, IGRASS 2010, 2761–2764
24	Wang S, Zhang F, Shao Y, Tian W, Gong H (2010). Microwave Remote Sensing for Marine Monitoring: An Example of Enteromorpha prolifera Bloom Monitoring. IEEE International Geoscience & Remote Sensing Symposium, IGRASS 2010, 4530–4533
25	Wang X H, Li L, Bao X, Zhao L D (2009). Economic cost of an algae bloom cleanup in China’s 2008 Olympic sailing venue. Eos (Wash DC), 90(28): 238–239 https://doi.org/10.1029/2009EO280002
26	Yamaguchi Y, Moriyama T, Ishido M, Yamada H (2005). Four-component scattering model for polarimetric SAR image decomposition. IEEE Trans Geosci Remote Sens, 43(8): 1699–1706 https://doi.org/10.1109/TGRS.2005.852084
27	Yamaguchi Y, Sato A, Boerner W M, Sato R, Yamada H (2011). Four-component scattering power decomposition with rotation of coherency matrix. IEEE Transactions on Geoscience & Remote Sensing, 49(6): 2251–2258
28	Yamaguchi Y, Yajima Y, Yamada H (2006). A four-component decomposition of POLSAR images based on the coherency matrix. IEEE Geosci Remote Sens Lett, 3(3): 292–296 https://doi.org/10.1109/LGRS.2006.869986
29	Yoshida G, Uchimura M, Hiraoka M (2015). Persistent occurrence of floating Ulva green tide in Hiroshima Bay, Japan: seasonal succession and growth patterns of Ulva pertusa and Ulva spp. (Chlorophyta, Ulvales). Hydrobiologia, 758(1): 223–233 https://doi.org/10.1007/s10750-015-2292-3
30	Zhang B, Perrie W, Li X, Pichel W G (2011). Mapping sea surface oil slicks using RADARSAT-2 quad-polarization SAR image. Geophys Res Lett, 38: L10602 https://doi.org/10.1029/2011GL047013
31	Zhang J H, Huo Y Z, Zhang Z L, Yu K F, He Q, Zhang L H, Yang L L, Xu R, He P M (2013). Variations of morphology and photosynthetic performances of Ulva prolifera during the whole green tide blooming process in the Yellow Sea. Mar Environ Res, 92(6): 35–42 https://doi.org/10.1016/j.marenvres.2013.08.009

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