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커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
초록· 키워드
오류제보하기
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.
목차
Abstract
1. Introduction
2. Study Area and Data
3. Seabed Rock Detection using Bathymetric Lidar
4. Seabed Rock Detection using the Hyperspectral Image
5. Data Fusion and Seabed Rock Detection
6. Conclusion
References
참고문헌 (19)
참고문헌 신청
Choi, H. (2014), A Study on Seacoast Land CoverClassification from Hyperspectral Images, Master`s thesis,Kyonggi University, Suwon, Korea, 32p.
Ciraolo, G., Cox, E., La Loggia, G., and Maltese, A.(2006), The classification of submerged vegetation usinghyperspectral MIVIS data, Annals of Geophysics, Vol. 49,No. 1, pp. 287-294.
Collins, B., Penley, M., and Monteys, X. (2007), Lidar seabedclassification: New process for generation of seabedclasses, Hydro International, Netherland, http://www.hydro-international.com/articles (last date accessed: 22March 2016).
Hedley, J. D. (2013), Hyperspectral applications, In: Goodman,J. A., Purkis, S. J. and Phinn, S. R. (eds.), Coral Reef RemoteSensing: A Guide for Mapping, Monitoring and Management,Springer, Dordrecht, Germany, pp. 79-112.
Hedley, J.D., Harborne, A.R., and Mumby, P.J. (2005), Simpleand robust removal of sun glint for mapping shallow waterbenthos, International Journal of Remote Sensing, Vol. 26,No. 10, pp. 2107-2112.
Ciraolo, G., Cox, E., La Loggia, G., and Maltese, A.(2006), The classification of submerged vegetation usinghyperspectral MIVIS data, Annals of Geophysics, Vol. 49,No. 1, pp. 287-294.
Collins, B., Penley, M., and Monteys, X. (2007), Lidar seabedclassification: New process for generation of seabedclasses, Hydro International, Netherland, http://www.hydro-international.com/articles (last date accessed: 22March 2016).
Hedley, J. D. (2013), Hyperspectral applications, In: Goodman,J. A., Purkis, S. J. and Phinn, S. R. (eds.), Coral Reef RemoteSensing: A Guide for Mapping, Monitoring and Management,Springer, Dordrecht, Germany, pp. 79-112.
Hedley, J.D., Harborne, A.R., and Mumby, P.J. (2005), Simpleand robust removal of sun glint for mapping shallow waterbenthos, International Journal of Remote Sensing, Vol. 26,No. 10, pp. 2107-2112.
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UCI(KEPA) : I410-ECN-0101-2016-533-002817591