Analysis of ship wake features and extraction of ship motion parameters from SAR images in the Yellow Sea

doi:10.1007/s11707-018-0743-7

Front. Earth Sci.

2019, Vol. 13

Issue (3) : 588-595 https://doi.org/10.1007/s11707-018-0743-7

RESEARCH ARTICLE

Analysis of ship wake features and extraction of ship motion parameters from SAR images in the Yellow Sea

Kaiguo FAN^1,², Huaguo ZHANG², Jianjun LIANG³, Peng CHEN²(

), Bojian XU¹, Ming ZHANG⁴

¹. P.O. Box 5136, No. 22, Beiqing Road, Haidian District, Beijing 100094, China
². State Key Laboratory of Satellite Ocean Environment Dynamics, Second Institute of Oceanography, Ministry of Natural Resources, Hangzhou 310012, China
³. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100094, China
⁴. College of Information Science and Engineering, Linyi University, Linyi 276000, China

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Abstract

The identifying features of ship wakes in synthetic aperture radar (SAR) remote sensing images are of great importance for detecting ships and for extracting ship motion parameters. A statistical analysis was conducted on the identifying features of ship wakes in SAR images in the Yellow Sea. In this study, 1091 ship wake sub-images were selected from 327 SAR images in the Yellow Sea near Qingdao. Analysis of the identifying features of ship wakes in SAR images revealed that both turbulent wakes and Kelvin wakes account for the majority of ship wakes, with turbulent wakes occurring approximately four times as frequently as Kelvin wakes. Narrow-V wakes and internal wave wakes were comparatively rare, which is due to the peculiarities of the radar system parameters and marine environments required to observe these wakes. Additionally, we extracted ship motion parameters from four types of ship wakes in the SAR images. Specifically, internal wave wakes in SAR images in the Yellow Sea were also used to extract ship motion parameters. Validation of the extracted parameters indicated that the extraction of these parameters from ship wakes is a viable and accurate approach for the acquisition of ship motion parameters. These results provide a solid foundation for the commercialization of SAR-based technologies for detecting ships and extracting ship motion parameters.

Keywords synthetic aperture radar remote sensing ship wake ship motion parameter

Corresponding Author(s): Peng CHEN

Just Accepted Date: 16 November 2018 Online First Date: 28 February 2019 Issue Date: 15 October 2019

Cite this article:

Kaiguo FAN,Huaguo ZHANG,Jianjun LIANG, et al. Analysis of ship wake features and extraction of ship motion parameters from SAR images in the Yellow Sea[J]. Front. Earth Sci., 2019, 13(3): 588-595.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-018-0743-7
https://academic.hep.com.cn/fesci/EN/Y2019/V13/I3/588

Tab.1 Calculated ship wake probabilities

Fig.1 Representative environmental satellite (ENVISAT) advanced synthetic aperture radar (ASAR) images of ship wakes in the Yellow Sea; pixel size is 12.5 m. (a) Narrow-V wake imaged at 10:06 on April 21, 2006; (b) Kelvin wake imaged at 21:40 on August 17, 2007; (c) turbulent wake imaged at 21:37 on July 29, 2007; (d) internal wave wake imaged at 21:43, July 27, 2005.

Fig.2 An ENVISAT ASAR IMP image (100 km × 100 km) of the Yellow Sea near Qingdao acquired at 10:06 on April 21, 2006, and one ERS-2 SAR image was also imaged of the same area 29 minutes later than ENVISAT ASAR.

Fig.3 Turbulent wake induced by a ship in sequential SAR images (10 km × 10 km). (a) The ENVISAT SAR image at 10:06 on April 21, 2006 and (b) the European Remote-Sensing Satellite 2 (ERS-2) SAR image at 10:35 on April 21, 2006.

Fig.4 (a) Internal wave wakes imaged by ENVISAR ASAR in the Yellow Sea on April 28, 2006 and (b) the corresponding temperature (T), salinity (S), and density (D) profiles from temperature, conductivity, and depth (CTD) data.

1	J Q Ai, X Y Qi, W Yu, F Liu (2010). A new ship wake CFAR detection algorithm in SAR images based on image segmentation and normalized Hough transform. Journal of Electronic & Information Technology, 32(11): 2668–2673 https://doi.org/10.3724/SP.J.1146.2009.01527
2	Q An, Z Pan, H You (2018). Ship detection in Gaofen-3 SAR images based on sea clutter distribution analysis and deep convolutional neural network. Sensors (Basel), 18(2): 334 https://doi.org/10.3390/s18020334
3	A Arnold-Bos, A Martin, A Khenchaf (2007). Obtaining a ship’s speed and direction from its Kelvin wake spectrum using stochastic matched filtering. Proc Int Geosci Remote Sens Symp, 1106–1109
4	F Biondi (2018). Low-rank plus sparse decomposition and localized radon transform for ship-wake detection in synthetic aperture radar images. IEEE Geosci Remote Sens Lett, doi: 10.1109/LGRS. 2018.2868365
5	K G Fan, B Fu, Y Gu, X Yu, T Liu, A Shi, K Xu, X Gan (2015). Internal wave parameters retrieval from space-borne SAR image. Front Earth Sci, 9(4): 700–708 https://doi.org/10.1007/s11707-015-0506-7
6	M D Graziano, M D’Errico, G Rufino (2016). Ship heading and velocity analysis by wake detection in SAR images. Acta Astronaut, 128: 72–82 https://doi.org/10.1016/j.actaastro.2016.07.001
7	I Hennings, R Romeiser, W Alpers, A Viola (1999). Radar imaging of Kelvin arms of ship wakes. Int J Remote Sens, 20(13): 2519–2543 https://doi.org/10.1080/014311699211912
8	G G Hogan, R D Chapman, G Watson, D R Thompson (1994). Observations of ship-generated internal waves in SAR images from Loch Linnhe, Scotland, and comparison with theory and in situ internal wave measurements. IEEE Trans Geosci Remote Sens, 34(2): 1363–1369
9	C R Jackson, J R Apel (2004). Synthetic Aperture Radar Marine User’s Manual. National Oceanic and Atmospheric Administration
10	J D Lyden, R R, Hammond D R Lyzenga, R A Shuchman (1988). Synthetic aperture radar imaging of surface ship wakes. Journal of Geophysical Research, 1988, 193: 12293–12300
11	C Melsheimer, H Lim, C M Shen (1999). Observation and analysis of ship wakes in ERS-SAR and SPOT image. Proceedings of the 20th Asian Conference on Remote Sensing, 22–25
12	W H Munk (1987). Ship from space. Proceedings of the Royal Society London, A (412): 231–254
13	K Ouchi (2013). Recent trend and advance of synthetic aperture radar with selected topics. Remote Sensing, 5: 716–807 https://doi.org/10.3390/rs5020716
14	K Ouchi, N R Stapleton, B C Barber (1997). Multi-frequency SAR images of ship-generated internal waves. Int J Remote Sens, 18(18): 3709–3718 https://doi.org/10.1080/014311697216568
15	K Oumansour, Y Wang, J Saillard (1994). Application of polarimetric synthetic aperture radar to a ship wake in simulation. Symposium on Antenna Technology and Applied Electromagnetics, Ottawa, Canada, 661–664
16	A Panico, M D Graziano, A Renga (2017). SAR-based vessel velocity estimation from partially imaged Kelvin pattern. IEEE Geosci Remote Sens Lett, 14(11): 2067–2071 https://doi.org/10.1109/LGRS.2017.2751083
17	A, Skoelv T Wahl, S Eriksen (1988). Simulation of SAR imaging of ship wakes. Proceedings of IGARSS’88 Symposium, 13–16
18	N R Stapleton (1997). Ship wakes in radar imagery. Int J Remote Sens, 18(6): 1381–1386
19	Y Sun, P Liu, Y Q Jin (2018). Ship wake components: isolation, reconstruction, and characteristics analysis in spectral, spatial, and TerraSAR-X image domains. IEEE Trans Geosci Remote Sens, 56(7): 4209–4224 https://doi.org/10.1109/TGRS.2018.2828833
20	C V Swanson (1984). Radar Observability of Ship Wakes. Report of Cortana Corporation
21	B Tings, D Velotto (2018). Comparison of ship wake detectability on C-band and X-band SAR. Int J Remote Sens, 39(13): 4451–4468 https://doi.org/10.1080/01431161.2018.1425568
22	J F Vesecky, R H Stewart (1982). The observation of ocean surface phenomena using imagery from the Seasat synthetic aperture radar: an assessment. J Geophys Res, 87(C5): 3397–3430 https://doi.org/10.1029/JC087iC05p03397
23	L L Wang, H X Chen (2009). Multi-ship wakes detection method based on recursive modified Hough transform field. J Syst Eng Electron, 31(4): 834–837
24	Z Xu, B, Tang S Cheng (2018). Faint ship wake detection in PolSAR images. IEEE Geoscience & Remote Sensing Letters, 2018, 15(7): 1055–1059
25	G Zilman, T Miloh (1997). Radar backscatter of a V-like ship wake from a sea surface covered by surfactants. Twenty-first Symposium on Naval hydrodynamics
26	G Zilman, A Zapolski, M Marom (2004). The speed and beam of a ship from its wake’s SAR images. IEEE Trans Geosci Remote Sens, 42(10): 2335–2343 https://doi.org/10.1109/TGRS.2004.833390
27	G Zilman, A Zapolski, M Marom (2015). On detectability of a ship’s Kelvin wake in simulated SAR images of rough sea surface. IEEE Trans Geosci Remote Sens, 53(2): 609–619 https://doi.org/10.1109/TGRS.2014.2326519

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