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Detection of Seabed Rock Using Airborne Bathymetric Lidar and Hyperspectral Data in the East Sea Coastal Area
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 Title & Authors
Detection of Seabed Rock Using Airborne Bathymetric Lidar and Hyperspectral Data in the East Sea Coastal Area
Shin, Myoung Sig; Shin, Jung Il; Park, In Sun; Suh, Yong Cheol;
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 Abstract
The distribution of seabed rock in the coastal area is relevant to navigation safety and development of ocean resources where it is an essential hydrographic measurement. Currently, the distribution of seabed rock relies on interpretations of water depth data or point based bottom materials survey methods, which have low efficiency. This study uses the airborne bathymetric Lidar data and the hyperspectral image to detect seabed rock in the coastal area of the East Sea. Airborne bathymetric Lidar data detected seabed rocks with texture information that provided 88% accuracy and 24% commission error. Using the airborne hyperspectral image, a classification result of rock and sand gave 79% accuracy, 11% commission error and 7% omission error. The texture data and hyperspectral image were fused to overcome the limitations of individual data. The classification result using fused data showed an improved result with 96% accuracy, 6% commission error and 1% omission error.
 Keywords
Seabed rock;Detection;Bathymetric Lidar;Hyperspectral Image;
 Language
English
 Cited by
 References
1.
Choi, H. (2014), A Study on Seacoast Land Cover Classification from Hyperspectral Images, Master`s thesis, Kyonggi University, Suwon, Korea, 32p.

2.
Ciraolo, G., Cox, E., La Loggia, G., and Maltese, A. (2006), The classification of submerged vegetation using hyperspectral MIVIS data, Annals of Geophysics, Vol. 49, No. 1, pp. 287-294.

3.
Collins, B., Penley, M., and Monteys, X. (2007), Lidar seabed classification: New process for generation of seabed classes, Hydro International, Netherland, http://www.hydro-international.com/articles (last date accessed: 22 March 2016).

4.
Hedley, J. D. (2013), Hyperspectral applications, In: Goodman, J. A., Purkis, S. J. and Phinn, S. R. (eds.), Coral Reef Remote Sensing: A Guide for Mapping, Monitoring and Management, Springer, Dordrecht, Germany, pp. 79-112.

5.
Hedley, J.D., Harborne, A.R., and Mumby, P.J. (2005), Simple and robust removal of sun glint for mapping shallow water benthos, International Journal of Remote Sensing, Vol. 26, No. 10, pp. 2107-2112. crossref(new window)

6.
Jensen, J. R. (2005), Introductory Digital Image Processing:A Remote Sensing Perspective, 3rd Edition, Prentice Hall, Upper Saddle River, N.J.

7.
Kim, H. (2014), Extraction of Geospatial Information of Coastal Area Using Airborne Hyperspectral Imagery and LiDAR DEM, Ph.D. dissertation, Kumoh National Institute of Technology, Gumi, Korea, 123p.

8.
Kim, T., Choi, Y., Choi, J., Kwon, M., and Park, H. (2013), Comparison between in situ survey and satellite imagery with regard to coastal habitat distribution patterns in Weno, Micronesia, Ocean and Polar Research, Vol. 35, No. 4, pp. 395-405. crossref(new window)

9.
Lyzenga, D. R. (1978), Passive remote sensing techniques for mapping water depth and bottom features, Applied Optics, Vol. 17, No. 3, pp. 379-383. crossref(new window)

10.
Mishra, D.R., Narumalani, S., Rundquist, D., Lawson, M., and Perk, R. (2007), Enhancing the detection and classification of coral reef and associated benthic habitats: A hyperspectral remote sensing approach, Journal of Geophysical Research, Vol. 112, No. C8.

11.
NOAA Coastal Services Center (2010), Lake Michigan Basin Land Cover Change Report, Report, NOAA Coastal Services Center, USA, pp. 1-13.

12.
Oh, Y., Kim, B., Park, B., Choi, Y., and Nam, S. (2004), A study on the seabed information of the Korean coast, Proceedings of the Korean Association of Geographic Information Studies Conference, Korea Spatial Information Society, 1 March, Seoul, Korea, pp. 273-278.

13.
Pittman, S.J., Costa, B., and Wedding, L.M. (2013), LiDAR applications, In: Goodman, J.A., Purkis, S.J., and Phinn, S.R. (eds.), Coral Reef Remote Sensing: A Guide for Mapping, Monitoring and Management, Springer, Dordrecht, Germany, pp. 145-174.

14.
Seo, D. and Kim, J. (2008), Extraction of water depth in coastal area using EO-1 Hyperion imagery, The Journal of The Korean Institute of Maritime Information and Communication Sciences, Vol. 12, No. 4, pp. 716-723.

15.
Shafri, H.Z.M., Suhaili, A., and Mansor, S. (2007), The performance of maximum likelihood, spectral angle mapper, neural network and decision tree classifiers in hyperspectral image analysis, Journal of Computer Science, Vol. 3, No. 6, pp. 419-423. crossref(new window)

16.
Tamir, C. and Arnon, K. (2015), Ground-level classification of a coral reef using a hyperspectral camera, Remote Sensing, Vol. 7, No. 6, pp. 7521-7544. crossref(new window)

17.
Wozencraft, J.M. and Park, J.Y. (2013), Integrated LiDAR and hyperspectral, In: Goodman, J. A., Purkis, S. J. and Phinn, S. R. (eds.), Coral Reef Remote Sensing: A Guide for Mapping, Monitoring and Management, Springer, Dordrecht, Germany, pp. 175-191.

18.
Yang, C., Goolsby, J. A., Everitt, J. H., and Du, Q. (2012), Applying six classifiers to airborne hyperspectral imagery for detecting giant reed, Geocarto International, Vol. 27, No. 5, pp. 413-424. crossref(new window)

19.
Zavalas, R., Ierodiaconou, D., Ryan, D., Rattray, A., and Monk, J. (2014), Habitat classification of temperate marine macroalgal communities using bathymetric LiDAR, Remote Sensing, Vol. 6, No. 3, pp. 2154-2175. crossref(new window)