Journal of the Korean Association of Geographic Information Studies (한국지리정보학회지)

The Korean Association of Geographic Information Studies (한국지리정보학회)
권호 표지
DOI QR코드

DOI QR Code

Current Status of Hyperspectral Data Processing Techniques for Monitoring Coastal Waters

연안해역 모니터링을 위한 초분광영상 처리기법 현황

  • Kim, Sun-Hwa (Korea Ocean Satellite Research Center, Korea Institute of Ocean Science and Technology) ;
  • Yang, Chan-Su (Korea Ocean Satellite Research Center, Korea Institute of Ocean Science and Technology)
  • 김선화 (한국해양과학기술원 해양위성연구센터) ;
  • 양찬수 (한국해양과학기술원 해양위성연구센터)
  • Received : 2014.10.28
  • Accepted : 2015.01.26
  • Published : 2015.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

In this study, we introduce various hyperspectral data processing techniques for the monitoring of shallow and coastal waters to enlarge the application range and to improve the accuracy of the end results in Korea. Unlike land, more accurate atmospheric correction is needed in coastal region showing relatively low reflectance in visible wavelengths. Sun-glint which occurs due to a geometry of sun-sea surface-sensor is another issue for the data processing in the ocean application of hyperspectal imagery. After the preprocessing of the hyperspectral data, a semi-analytical algorithm based on a radiative transfer model and a spectral library can be used for bathymetry mapping in coastal area, type classification and status monitoring of benthos or substrate classification. In general, semi-analytical algorithms using spectral information obtained from hyperspectral imagey shows higher accuracy than an empirical method using multispectral data. The water depth and quality are constraint factors in the ocean application of optical data. Although a radiative transfer model suggests the theoretical limit of about 25m in depth for bathymetry and bottom classification, hyperspectral data have been used practically at depths of up to 10 m in shallow and coastal waters. It means we have to focus on the maximum depth of water and water quality conditions that affect the coastal applicability of hyperspectral data, and to define the spectral library of coastal waters to classify the types of benthos and substrates.

본 연구에서는 초분광영상의 국내 연안 활용 범위 확대 및 정확성 향상을 위해, 국외 연안지역에 대한 항공기 및 위성 탑재 초분광영상의 다양한 처리 기법을 소개한다. 육상과 달리, 가시광선 영역에서 미세한 반사율을 보이는 해양의 경우 보다 정밀한 대기보정이 요구된다. 이와 함께, 태양-해수면-센서의 기하학적 특징으로 나타나는 태양광 정반사(sun-glint)와 같은 이상 현상을 제거하기 위한 다양한 기법도 개발되어 왔다. 대기 및 정반사 보정된 초분광영상은 연안지역의 수심추정과 산호와 같은 저서 생물 및 해저면 종류 분류, 저서 생물 상태 모니터링에 활용되는데, 주로 복사전달모델과 분광라이브러리에 기반을 둔 반분석적 기법을 사용한다. 이는 초분광영상의 많은 분광 정보를 활용하는 방법으로, 실험적 모델을 적용하는 다중분광자료에 비해 상대적으로 정확도가 높다. 광학영상의 해양활용에서 있어 수심 및 수질은 매우 중요한 제약점으로, 특히 복사전달모델에 기반을 둔 분석에 따르면 초분광영상은 최대 25m까지 수심측정이나 해저면 분류가 가능하다고 하나, 실제 많은 연구에서 항공기 및 위성 탑재 초분광영상은 수심 10m 이내의 연안지역에서 활용되고 있다. 이와 같은 연구결과를 바탕으로 국내 연안지역의 초분광영상자료의 정확하고 정량적인 연안 활용을 위해서는 최대 탐지 가능한 수심 및 수질조건 등에 대한 분석이 필요하다는 것을 확인하였다. 또한 국내 연안지역에 대해 분류 가능한 저서 생물과 해저면의 분류 및 분광라이브러리 구축의 필요성을 제시하였다.

Keywords

References

  1. Adler-Golden, S.M., P.K. Acharya, A. Berk, M.W. Matthew and D. Gorodetzky. 2005. Remote bathymetry of the littoral zone from AVIRIS, LASH, and QuickBird imagery. IEEE Transactions on Geoscience and Remote Sensing 43(2):337-347. https://doi.org/10.1109/TGRS.2004.841246
  2. Andrefouet, S., J.A. Robinson, C. Hu, G. Feldman, B. Salvat, C. Payri, and F.E. Muller-Karger. 2003. Influence of the spatial resolution of SeaWiFS, Landsat 7, SPOT and international space station data on determination of landscape parameters of Pacific ocean atolls. Canadian Journal of Remote Sensing 29(2):210-218. https://doi.org/10.5589/m02-086
  3. Brando, V.E., J.M. Anstee, M. Wettle, A.G. Dekker, S.R. Phinn and C. Roeflsema. 2009. A physics based retrieval and quality assessment of bathymetry from suboptimal hyperspectral data. Remote Sensing of Environment 113:755-770. https://doi.org/10.1016/j.rse.2008.12.003
  4. Ceyhun, O. and A. Yalcin. 2010. Remote sensing of water depths in shallow waters via artificial neural networks. Estuarine, Coastal and Shelf Science 89:89-96. https://doi.org/10.1016/j.ecss.2010.05.015
  5. Clarisse, L., F. Prata, J.L. Lacour, D. Hurtmans, C. Clerbaux and P.F. Coheur. 2010. A correlation method for volcanic ash detection using hyperspectral infrared measurements. Geophysical Research Letters 37(19):L19806. https://doi.org/10.1029/2010GL044828
  6. Clark, C.D., P.J. Mumby, J.R.M. Chisholm, J. Jaubert and S. Andrefouet. 2000. Spectral discrimination of coral mortality states following a severe bleaching event. International Journal of Remote Sensing 21(11):2321-2327. https://doi.org/10.1080/01431160050029602
  7. Cox, C. and W. Munk. 1954. Statistics of the sea surface derived from sun glitter. Journal of Marine Resarch 13: 198-227.
  8. Cox, C. and W. Munk. 1956. Slopes of the sea surface deduced from photographs of sun glitter. Bulletin of the Scripps Institution of Oceanography 6:401-488.
  9. Dekker, A.G., S.R. Phinn, J. Anstee, P. Bissett, V.E. Brando, B. Casey, P. Fearns, J. Hedley, W. Klonowski, Z.P. Lee, M. Lynch, M. Lyons, C. Mobley and C. Roelfsema. 2011. Intercomparison of shallow water bathymetry, hydrooptics, and benthos mapping techniques in Australian and Caribbean coastal environments. Limnology and Oceanography: Methods 9:396-425. https://doi.org/10.4319/lom.2011.9.396
  10. Fearns, P.R.C., W. Klonowski, R.C. Babcock, P. England, and J. Phillips. 2011. Shallow water substrate mapping using hyperspectral remote sensing. Continental Shelf Research 31:1249-1259. https://doi.org/10.1016/j.csr.2011.04.005
  11. Gagnon, P., R.E. Scheibling, W. Jones, and D. Tully. 2008. The role of digital bathymetry in mapping shallow marine vegetation from hyperspectral image data. International Journal of Remote Sensing 29(3):879-904. https://doi.org/10.1080/01431160701311283
  12. Gao, B.C., M.J. Montes, C.O. Davis and A.F.H. Goetz. 2009. Atmospheric correction algorithms for hyperspectral remote sensing data of land and ocean. Remote Sensing of Environment 113: S17-S24. https://doi.org/10.1016/j.rse.2007.12.015
  13. Gao, B.C., M.J. Montes, Z. Ahmad, and C.O. Davis. 2000. Atmospheric correction algorithm for hyperspectral remote sensing of ocean color from space. Applied Optics 39:887-896. https://doi.org/10.1364/AO.39.000887
  14. Gholamalifard, M., T. Kutser, A. Esmaili-Sari, A.A. Abkar and B. Naimi. 2013. Remotely sensed empirical modeling of bathymetry in the Southeastern Caspian Sea. Remote Sensing 5:2746-2762. https://doi.org/10.3390/rs5062746
  15. Goetz, A.F.H. 1991. Imaging spectrometry for studying earth, air, fire and water. EARSeL Advances in Remote Sensing 1:3-15.
  16. Goodman, J.A., Z. Lee and S.L. Ustin. 2008. Influence of atmospheric and sea-surface corrections on retrieval of bottom depth and reflectance using a semi-analytical model: a case study in Kaneohe bay, Hawaii. Applied Optics 47:F1-F11. https://doi.org/10.1364/AO.47.0000F1
  17. Gordon, H. and K. Voss. 1999. MODIS normalized water-leaving radiance algorithm. Theoretical Basis Document, vol.4, NASA. 39pp.
  18. Hedley, J.D., A.R. Harborne and P.J. Mumby. 2005. Simple and robust removal of sun glint for mapping shallow-water benthos. International Journal of Remote Sensing 26(10):2107-2112. https://doi.org/10.1080/01431160500034086
  19. Heege, T. and J. Fischer. 2000. Sun glitter correction in remote sensing imaging spectrometry. Proceedings of SPIE Ocean Optics XV Conference, Monaco, October 16-20, 2000.
  20. Hochberg, E.J., M.J. Atkinson and S. Andrefouet. 2003. Spectral reflectance of coral reef bottom-types worldwide and implications for coral reef remote sensing. Remote Sensing of Environment 85:159-173. https://doi.org/10.1016/S0034-4257(02)00201-8
  21. Holden, H. and E. LeDrew. 1999. Hyperspectral identification of coral reef features. International Journal of Remote Sensing 20(13):2545-2563. https://doi.org/10.1080/014311699211921
  22. Joyce, K.E. 2005. A method for mapping live coral cover using remote sensing. Ph.D. Thesis, The University of Queensland, St Lucia, Australia.
  23. Kay, S., J.D. Hedley and S. Lavender. 2009. Sun glint correction of high and low spatial resolution images of aquatic scenes: a review of methods for visible and near-infrared wavelengths. Remote Sensing 1:697-730. https://doi.org/10.3390/rs1040697
  24. KHOA(Korea Hydrographic and Oceanographic Administration) Ministry of Land, Transport and Maritime Affairs. 2011. Result report of detailed investigation in coastal waters. 333pp (국토해양부 국립해양조사원. 2011. 연안해역정밀조사 결과보고서. 333쪽).
  25. Kim, S.H., K.S. Lee, J.R. Ma and M.J. Kook. 2005. Current status of hyperspectral remote sensing: principle, data processing techniques, and applications. Korean Journal of Remote Sensing 21(4):341-369 (김선화, 이규성, 마정림, 국민정. 2005. 초분광 원격탐사의 특성, 처리기법 및 활용 현황. 대한원격탐사학회지 21(4):341-369). https://doi.org/10.7780/kjrs.2005.21.4.341
  26. Kim, S.H., S.J. Kang, J.H. Chi and K.S. Lee. 2007. Absolute atmospheric correction procedure for the EO-1 Hyperion data using MODTRAN code. Korean Journal of Remote Sensing 23(1):7-14. https://doi.org/10.7780/kjrs.2007.23.1.7
  27. Kim, T.W., G.J. We and Y.C. Suh. 2012. Correlation analysis with vegetation indices and vegetation-endmembers from airborne hyperspectral data in forest area. Journal of the Korean Association of Geographic Information Studies 15(3):52-65 (김태우, 위광재, 서용철. 2012. 산림지역의 항공기 탑재 하이퍼스펙트럴 영상에 대한 식생-Endmember와 식생지수의 상관 분석. 한국지리정보학회지 15(3):52-65). https://doi.org/10.11108/kagis.2012.15.3.052
  28. Kim, T.W., D.J. Choi, G.J. We and Y.C. Suh. 2013a. Detection of small green space in an urban area using airborne hyperspectral imagery and spectral angle mapper. Journal of the Korean Association of Geographic Information Studies 16(2):88-100 (김태우, 최돈정, 위광재, 서용철. 2013a. 분광각매퍼 기법을 적용한 항공기 탑재 초분광영상의 소규모 녹지공간 탐지. 한국지리정보학회지 16(2):88-100). https://doi.org/10.11108/kagis.2013.16.2.088
  29. Kim, T.W., G.J. We and Y.C. Suh. 2013b. An adequate band selection for vegetation index of CASI-1500 airborne hyperspectral imagery using image differencing and spectral derivative. Journal of the Korean Association of Geographic Information Studies 16(4):16-28 (김태우, 위광재, 서용철. 2013b. 차연산과 분광미분을 이용한 항공 초분광영상의 식생지수 산출 적절밴드 선택. 한국지리정보학회지 16(4):16-28). https://doi.org/10.11108/kagis.2013.16.4.016
  30. Kim, T.W., G.J. We and Y.C. Suh. 2014. Airborne hyperspectral imagery availability to estimate inland water quality parameter. Korean Journal of Remote Sensing 30(1):61-73 (김태우, 신한섭, 서용철. 2014. 수질 매개변수 추정에 있어서 항공 초분광영상의 가용성 고찰. 대한원격탐 사학회지 30(1):61-73). https://doi.org/10.7780/kjrs.2014.30.1.6
  31. Kutser, T., A.G. Dekker and W. Skirving. 2003. Modeling spectral discrimination of Great Barrier Reef benthic communities by remote sensing instruments. Limnology and Oceanography 48:497-510. https://doi.org/10.4319/lo.2003.48.1_part_2.0497
  32. Kutser, T., E. Vahtmae and J. Praks. 2009. A sun glint correction method for hyperspectral imagery containing areas with non-negligible water leaving NIR signal. Remote Sensing of Environment 113:2267-2274. https://doi.org/10.1016/j.rse.2009.06.016
  33. Kutser, T., I. Miller and D.L.B. Jupp. 2006. Mapping coral reef benthic substrates using hyperspectral spaceborne images and spectral libraries. Estuarine, Coastal and Shelf Science 70:449-460. https://doi.org/10.1016/j.ecss.2006.06.026
  34. Lavender, S., M. Pinkerton, G. Moore, J. Aiken and D. Blondeau-Patissier. 2005. Modification to the atmospheric correction of SeaWiFS ocean colour images over turbid waters. Continental Shelf Research 25:539-555. https://doi.org/10.1016/j.csr.2004.10.007
  35. Lee, Z. and K.L. Carder. 2002. Effect of spectral band numbers on the retrieval of water column and bottom properties from ocean color data. Applied Optics 41(12):2191-2201. https://doi.org/10.1364/AO.41.002191
  36. Lee, Z.P., K.L. Carder, C.D. Mobley, R.G. Steward and J.S. Patch. 1998. Hyperspectral remote sensing for shallow waters I, a semianalytical model. Applied Optics 37(27):6329-6338. https://doi.org/10.1364/AO.37.006329
  37. Lee, Z.P., K.L. Carder, R.F. Chen and T.G. Peacock. 2001. Properties of the water column and bottom derived from airborne visible infrared imaging spectrometer(AVIRIS) data. Journal of Geophysical Research 106:11639-11651. https://doi.org/10.1029/2000JC000554
  38. Leiper, I.A., S.R. Phinn, C.M. Roelfsema and K.E. Joyce. 2014. Mapping coral reef benthos, substrates, and bathymetry using compact airborne spectrographic imager(CASI) data. Remote Sensing 6:6423-6445. https://doi.org/10.3390/rs6076423
  39. Lyzenga, D.R. 1978. Passive remote sensing techniques for mapping water depth and bottom features. Applied Optics 17(3):379-383. https://doi.org/10.1364/AO.17.000379
  40. Lygenza, D.R., N.P. Malinas and F.J. Tanis. 2006. Multispectral bathymetry using a simple physically based algorithm. IEEE Transactions on Geoscience and Remote Sensing 44(8): 2251-2259. https://doi.org/10.1109/TGRS.2006.872909
  41. Ma, S., Z. Tao, X. Yang, Y. Yu, X. Zhou and Z. Li. 2014. Bathymetry retrieval from hyperspectral remote sensing data in optical-shallow water. IEEE Transactions on Geoscience and Remote Sensing 52(2):1205-1212. https://doi.org/10.1109/TGRS.2013.2248372
  42. Martorena, S., A. Morel and B. Gentili. 1994. Diffuse reflectance of oceanic shallow waters: influence of water depth and bottom albedo. Limnology and Oceanography 39:1689-1703. https://doi.org/10.4319/lo.1994.39.7.1689
  43. Mazel, C.H. 1995. Spectral measurements of fluorescence emission in Caribbean cnidarians. Marine Ecology Progress Series 120:185-191. https://doi.org/10.3354/meps120185
  44. Miksa, M., C. Haese and T. Heege. 2006. Time series of water constituents and primary production in Lake Constance using satellite data and a physically based modular inversion and processing system. Proc. Ocean Opt. Conf. XVIII, Oct. 9-13, 10pp.
  45. Min, J.E., J.H. Ryu, Y.H. Ahn, P. Shanmugam, P.Y. Deschamps and Z.P. Lee. 2010. Atmospheric and BRDF correction method for Geostationary Ocean Color Imagery(GOCI). Korean Journal of Remote Sensing 26(2):175-188 (민지은, 유주형, 안유환, P. Shanmugam, P.Y. Deschamps and Z.P. Lee. 2010. 정지궤도 해색탑재체(GOCI) 자료를 위한 대기 및 BRDF 보정 연구. 대한원격탐사학회지 26(2):175-188).
  46. Mishra, D.R., S. Narumalani, D. Rundquist, M. Lawson and R. Perk. 2007. Enhancing the detection and classification of coral reef and associated benthic habitats: a hyperspectral remote sensing approach. Journal of Geophysical Research 112, C08014, doi:10.1029/2006JC003892.
  47. Mishra, D., S. Narumalani, M. Lawson and D. Rundquist. 2004. Bathymetric mapping using IKONOS multispectral data. GIScience and Remote Sensing 41(4):301-321. https://doi.org/10.2747/1548-1603.41.4.301
  48. Morel, A., 1974. Optical properties of pure water and pure sea water, in Optical Aspects of Oceanography. In: N.G. Jerlov and E.S. Nielsen(ed.). Optical Aspects of Oceanography. Academic New York, USA, pp.1-24.
  49. Park, Y.J., H.J. Jang, Y.S. Kim, K.H. Baik and H.S. Lee. 2014. A research on the applicability of water quality analysis using the hyperspectral sensor. Journal of the Korean Society for Environmental Analysis 17(3):113-125 (박연정, 장현지, 김윤석, 백경희, 이희숙. 2014. 초분광센서를 이용한 수질 분석의 적용성에 관한 연구. 한국환경분석학회지 17(3):113-125).
  50. Phinn, S., C. Roelfsema, A. Dekker, V. Brando and J. Anstee. 2008. Mapping seagrass species, cover and biomass in shallow waters: an assessment of satellite multi-spectral and airborne hyper-spectral imaging systems in Moreton bay(Australia). Remote Sensing of Environment 112:3413-3425. https://doi.org/10.1016/j.rse.2007.09.017
  51. Polcyn, F.C., W.L. Brown and I.J. Sattinger. 1970. The measurement of water depth by remote sensing techniques, Institute of Science and Technology. Report 897326-F, Willow Run Laboratories, Univ. of Michigan, Ann Arnbor.
  52. Pope, R.M. and E.S. Fry. 1997. Absorption spectrum(380-700nm) of pure water. II. Integrating cavity measurements. Applied Optics 36(33): 8710-8723. https://doi.org/10.1364/AO.36.008710
  53. Richter, R., and D. Schlapfer. 2014. Atmospheric/topographic correction for airborne imagery. ATCOR-4 User Guide, v 6.3.1.
  54. Siegel, D.A., M. Wang, S. Maritorena, W. Robinson. 2000. Atmospheric correction of satellite ocean color imagery: the black pixel assumption. Applied Optics 39:3582-3591. https://doi.org/10.1364/AO.39.003582
  55. Sterckx, S. and W. Debruyn. 2004. A hyperspectral view of the North Sea. Proceedings of the Airborne Imaging Spectroscopy Workshop-Bruges, 8 October, 2004.
  56. Theriault, C., R.E. Scheibling, W. Jones, D. Tully. 2006. Mapping the distribution of an invasive marine alga(Codium fragile spp. Tomentosoides) in optically shallow coastal waters using the compact airborne spectrographic imager (CASI). Canadian Journal Remote Sensing 32:315-329. https://doi.org/10.5589/m06-027
  57. Vahtmae, E. and T. Kutser. 2013. Classifying the Baltic Sea shallow water habitats using image-based and spectral library methods. Remote Sensing 5: 2451-2474. https://doi.org/10.3390/rs5052451
  58. Vahtmae, E., T. Kutser, G. Martin and J. Kotta. 2006. Feasibility of hyperspectral remote sensing for mapping benthic macroalgal cover in turbid coastal waters : a Baltic Sea case study. Remote Sensing of Environment 202:342-351.

Cited by

  1. Change Detection Using Spectral Unmixing and IEA(Iterative Error Analysis) for Hyperspectral Images vol.31, pp.5, 2015, https://doi.org/10.7780/kjrs.2015.31.5.1
  2. 하이퍼센서 정보를 이용한 태화강지역의 비점오염원 분석 vol.20, pp.1, 2015, https://doi.org/10.11108/kagis.2017.20.1.056
  3. Himawari-8/AHI 기반 반사도 분광 라이브러리를 이용한 해양 구름 탐지 vol.33, pp.5, 2015, https://doi.org/10.7780/kjrs.2017.33.5.1.12
  4. Correlation Analysis Method for Ocean Monitoring Big Data in a Cloud Environment vol.82, pp.None, 2018, https://doi.org/10.2112/si82-003.1