Journal of the Korean earth science society (한국지구과학회지)

The Korean Earth Science Society (한국지구과학회)
권호 표지
DOI QR코드

DOI QR Code

The Ship Detection Using Airborne and In-situ Measurements Based on Hyperspectral Remote Sensing

초분광 원격탐사 기반 항공관측 및 현장자료를 활용한 선박탐지

  • Park, Jae-Jin (Department of Science Education, Seoul National University) ;
  • Oh, Sangwoo (Maritime Safety Research Division, Korea Research Institute of Ships and Ocean engineering) ;
  • Park, Kyung-Ae (Department of Earth Science Education/Research Institute of Oceanography, Seoul National University) ;
  • Foucher, Pierre-Yves (Theoretical and Applied Optics Department, ONERA) ;
  • Jang, Jae-Cheol (Department of Science Education, Seoul National University) ;
  • Lee, Moonjin (Maritime Safety Research Division, Korea Research Institute of Ships and Ocean engineering) ;
  • Kim, Tae-Sung (Maritime Safety Research Division, Korea Research Institute of Ships and Ocean engineering) ;
  • Kang, Won-Soo (Maritime Safety Research Division, Korea Research Institute of Ships and Ocean engineering)
  • 박재진 (서울대학교 과학교육과) ;
  • 오상우 (선박해양플랜트연구소 해양안전연구부) ;
  • 박경애 (서울대학교 지구과학교육과/해양연구소) ;
  • ;
  • 장재철 (서울대학교 과학교육과) ;
  • 이문진 (선박해양플랜트연구소 해양안전연구부) ;
  • 김태성 (선박해양플랜트연구소 해양안전연구부) ;
  • 강원수 (선박해양플랜트연구소 해양안전연구부)
  • Received : 2017.11.13
  • Accepted : 2017.12.14
  • Published : 2017.12.31
Download PDF
⟨ Previous Next ⟩

Abstract

Maritime accidents around the Korean Peninsula are increasing, and the ship detection research using remote sensing data is consequently becoming increasingly important. This study presented a new ship detection algorithm using hyperspectral images that provide the spectral information of several hundred channels in the ship detection field, which depends on high resolution optical imagery. We applied a spectral matching algorithm between the reflection spectrum of the ship deck obtained from two field observations and the ship and seawater spectrum of the hyperspectral sensor of an airborne visible/infrared imaging spectrometer. A total of five detection algorithms were used, namely spectral distance similarity (SDS), spectral correlation similarity (SCS), spectral similarity value (SSV), spectral angle mapper (SAM), and spectral information divergence (SID). SDS showed an error in the detection of seawater inside the ship, and SAM showed a clear classification result with a difference between ship and seawater of approximately 1.8 times. Additionally, the present study classified the vessels included in hyperspectral images by presenting the adaptive thresholds of each technique. As a result, SAM and SID showed superior ship detection abilities compared to those of other detection algorithms.

한반도 주변 해상사고가 증가함에 따라 원격탐사 자료를 활용한 선박탐지 연구의 중요성이 점점 더 강조되고 있다. 이 연구는 고해상도 광학영상에 의존하는 기존 선박탐지 분야에 수백 개 채널의 분광정보를 포함하는 초분광영상을 활용하여 새로운 선박탐지 알고리즘 제시하였다. 두 차례의 현장관측을 통해 측정한 선박 선체의 반사 스펙트럼과 AVIRIS (Airborne Visible/Infrared Imaging Spectrometer) 초분광센서 영상의 선박 및 해수 반사 스펙트럼 간의 분광정합 기법을 적용하였다. 총 다섯 개의 탐지 알고리즘 spectral distance similarity (SDS), spectral correlation similarity(SCS), spectral similarity value (SSV), spectral angle mapper (SAM), spectral information divergence (SID)를 사용하였다. SDS는 선박 일부가 해수로 탐지되는 오차를 나타내었고, SAM은 선박과 해수 사이에 약 1.8배의 차이를 나타내어 명확한 분류 결과를 보여주었다. 이와 더불어 본 연구에서는 각 기법의 최적 임계값을 제시하여 초분광 영상에 포함되어 있는 선박을 분류하였으며 그 결과 SAM, SID가 다른 탐지 알고리즘에 비해 우수한 선박탐지 능력을 보여주었다.

Keywords

References

  1. Barnsley, M.J., Settle, J.J., Cutter, M.A., Lobb, D.R., and Teston, F., 2004, The PROBA/CHRIS mission: A lowcost smallsat for hyperspectral multiangle observations of the earth surface and atmosphere. IEEE Transactions on Geoscience and Remote Sensing., 42(7), 1512-1520. https://doi.org/10.1109/TGRS.2004.827260
  2. Chang, C.I., 1999, Spectral information divergence for hyperspectral image analysis. In Geoscience and Remote Sensing Symposium, IGARSS'99 Proceedings., 1, 509-511.
  3. Corbane, C., Najman, L., Pecoul, E., Demagistri, L., and Petit, M., 2010, A complete processing chain for ship detection using optical satellite imagery. International Journal of Remote Sensing, 31(22), 5837-5854. https://doi.org/10.1080/01431161.2010.512310
  4. Datt, B., McVicar, T.R., Van Niel, T.G., Jupp, D.L., and Pearlman, J.S., 2003, Preprocessing EO-1 Hyperion hyperspectral data to support the application of agricultural indexes. IEEE Transactions on Geoscience and Remote Sensing, 41(6), 1246-1259. https://doi.org/10.1109/TGRS.2003.813206
  5. Eldhuset, K., 1996, An automatic ship and ship wake detection system for spaceborne SAR images in coastal regions. IEEE Transactions on Geoscience and Remote Sensing, 34(4), 1010-1019. https://doi.org/10.1109/36.508418
  6. Gao, G., 2011, A parzen-window-kernel-based CFAR algorithm for ship detection in SAR images. IEEE Geoscience and Remote Sensing Letters, 8(3), 557-561. https://doi.org/10.1109/LGRS.2010.2090492
  7. Goetz, A.F., 2009, Three decades of hyperspectral remote sensing of the Earth: A personal view. Remote Sensing of Environment, 113, S5-S16. https://doi.org/10.1016/j.rse.2007.12.014
  8. Goodenough, D.G., Dyk, A., Niemann, K.O., Pearlman, J.S., Chen, H., Han, T., and West, C., 2003, Processing Hyperion and ALI for forest classification. IEEE Transactions on Geoscience and Remote Sensing, 41(6), 1321-1331. https://doi.org/10.1109/TGRS.2003.813214
  9. Govender, M., Chetty, K., and Bulcock, H., 2007, A review of hyperspectral remote sensing and its application in vegetation and water resource studies. Water SA, 33(2), 145-151.
  10. Green, R.O., Eastwood, M.L., Sarture, C.M., Chrien, T.G., Aronsson, M., Chippendale, B.J., and Olah, M.R., 1998, Imaging spectroscopy and the airborne visible/infrared imaging spectrometer (AVIRIS). Remote Sensing of Environment,, 65(3), 227-248. https://doi.org/10.1016/S0034-4257(98)00064-9
  11. Homayouni, S. and Roux, M., 2004. Hyperspectral image analysis for material mapping using spectral matching. In ISPRS Congress Proceedings, Vol(20).
  12. Kim, M., Yim, U.H., Hong, S.H., Jung, J.H., Choi, H.W., An, J., and Shim, W.J., 2010, Hebei Spirit oil spill monitored on site by fluorometric detection of residual oil in coastal waters off Taean, Korea. Marine Pollution Bulletin, 60(3), 383-389. https://doi.org/10.1016/j.marpolbul.2009.10.015
  13. Kim, T.S., Park, K.A., Li, X., Lee, M., Hong, S., Lyu, S.J., and Nam, S., 2015, Detection of the Hebei Spirit oil spill on SAR imagery and its temporal evolution in a coastal region of the Yellow Sea. Advances in Space Research, 56(6), 1079-1093. https://doi.org/10.1016/j.asr.2015.05.040
  14. Kumar, A.S., Keerthi, V., Manjunath, A.S., van der Werff, H., and van der Meer, F., 2010, Hyperspectral image classification by a variable interval spectral average and spectral curve matching combined algorithm. International Journal of Applied Earth Observation and Geoinformation, 12(4), 261-269. https://doi.org/10.1016/j.jag.2010.03.004
  15. Lee, E.B., Yun, J.H., and Chung, S.T., 2012, A study on the development of the response resource model of hazardous and noxious substances based on the risks of marine accidents in Korea. Journal of Navigation and Port Research, 36(10), 857-864. https://doi.org/10.5394/KINPR.2012.36.10.857
  16. Lee, M.S., Park, K.A., Lee, H.R., Park, J.J., Kang, C.K., and Lee, M., 2016, Detection and dispersion of thick and film-like oil spills in a coastal bay using satellite optical images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(11), 5139-5150. https://doi.org/10.1109/JSTARS.2016.2577597
  17. Liao, M., Wang, C., Wang, Y., and Jiang, L., 2008, Using SAR images to detect ships from sea clutter. IEEE Geoscience and Remote Sensing Letters, 5(2), 194-198. https://doi.org/10.1109/LGRS.2008.915593
  18. Lu, Y., Tian, Q., Wang, X., Zheng, G., and Li, X., 2013, Determining oil slick thickness using hyperspectral remote sensing in the Bohai Sea of China. International Journal of Digital Earth, 6(1), 76-93. https://doi.org/10.1080/17538947.2012.695404
  19. Mattyus, G., 2013, Near real-time automatic vessel detection on optical satellite images. In ISPRS Hannover Workshop, 233-237 pp.
  20. Proia, N. and Page, V., 2010, Characterization of a bayesian ship detection method in optical satellite images. IEEE Geoscience and Remote Sensing Letters, 7(2), 226-230. https://doi.org/10.1109/LGRS.2009.2031826
  21. Schwarz, J. and Staenz, K., 2001, Adaptive threshold for spectral matching of hyperspectral data. Canadian Journal of Remote Sensing, 27(3), 216-224. https://doi.org/10.1080/07038992.2001.10854938
  22. Sridhar, B.M., Vincent, R.K., Witter, J.D., and Spongberg, A.L., 2009, Mapping the total phosphorus concentration of biosolid amended surface soils using LANDSAT TM data. Science of the Total Environment, 407(8), 2894-2899. https://doi.org/10.1016/j.scitotenv.2009.01.021
  23. Sweet, J. N., 2003, The spectral similarity scale and its application to the classification of hyperspectral remote sensing data. In Advances in Techniques for Analysis of Remotely Sensed Data, 2003 IEEE Workshop, 92-99 pp.
  24. Wackerman, C.C., Friedman, K.S., Pichel, W.G., Clemente-Colon, P., and Li, X., 2001, Automatic detection of ships in RADARSAT-1 SAR imagery. Canadian Journal of Remote Sensing, 27(5), 568-577. https://doi.org/10.1080/07038992.2001.10854896
  25. Yan, L., Noro, N., Takara, Y., Ando, F., and Yamaguchi, M., 2015, Using hyperspectral image enhancement method for small size object detection on the sea surface. In Image and Signal Processing for Remote Sensing XXI, 9643 pp.
  26. Yang, G., Li, B., Ji, S., Gao, F., and Xu, Q., 2014, Ship detection from optical satellite images based on sea surface analysis. IEEE Geoscience and Remote Sensing Letters, 11(3), 641-645. https://doi.org/10.1109/LGRS.2013.2273552

Cited by

  1. A Methodology of Ship Detection Using High-Resolution Satellite Optical Image vol.39, pp.3, 2018, https://doi.org/10.5467/JKESS.2018.39.3.241