[논문]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

doi:10.7848/ksgpc.2016.34.2.143

Detection of Seabed Rock Using Airborne Bathymetric Lidar and Hyperspectral Data in the East Sea Coastal Area 원문보기

한국측량학회지 = Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography, v.34 no.2, 2016년, pp.143 - 151

Shin, Myoung Sig (Korea Hydrographic and Oceanographic Agency) , Shin, Jung Il (Research Institute, Geostory Inc.) , Park, In Sun (Korea Hydrographic and Oceanographic Agency) , Suh, Yong Cheol (Dept. of Civil Engineering, Pukyung National University)

Abstract ▼ AI-Helper

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.

주제어

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문제 정의

This study attempted to suggest an efcient and accurate method for investigating rock distribution in coastal seabed using airborne remote sensing data. For this, texture data from bathymetric Lidar data and hyperspectral image was used individually to nd out the limits and possibilities of their use.

제안 방법

Thus, to estimate EM energy reected from the seabed, the water column absorption must be normalized in relation to water depth. In this study, the water column absorption effect was corrected for the water reectance of the hyperspectral image with 1m depth interval. Then a regression model was developed which showed the exponential relationship between water depth and water reectance for the same seabed material.
In this study, the water column absorption effect was corrected for the water reectance of the hyperspectral image with 1m depth interval. Then a regression model was developed which showed the exponential relationship between water depth and water reectance for the same seabed material. Finally, the regression model was inversely transformed into a logarithmic model for water column correction which was applied to whole image.
This study applied the maximum-minimum operator for the water depth from bathymetric Lidar data to convert it to texture data. Here, the size of the moving window was applied diversely as 3 x 3, 7 x 7, and 9 x 9, and the optimum window size was decided as 7 x 7.
Data fusion is a method to increase the accuracy of seabed mapping (Wozencraft and Park, 2013). This study attempted fusion of texture data and the hyperspectral image to overcome the uncertainty from water depth in the hyperspectral image and the limitation of spectral information in Lidar data. The texture data was added as a band in the hyperspectral image, that had 49 bands consisting of 48 spectral bands and 1 texture band.
This study attempts to detect seabed rock of the East Sea using airborne hyperspectral image and bathymetric Lidar data. To increase the accuracy, fused data that integrate the hyperspectral image and the seabed topographic (texture) data are suggested for detecting seabed rock.

대상 데이터

The airborne bathymetric Lidar data used in this study was acquired in September of 2013 with the CZMIL (Coastal Zone Mapping Imaging LiDAR) sensor of Optech Inc. in Canada. The ight altitude was 400m, and the laser scanning type was circular.
The study area is the coast from Sacheonjin Port to Gyeongpo Beach in Gangneung-si, Gangwon-do. The water in this area is comparatively clear, and the seabed material is composed of sand and rock.
This study attempted fusion of texture data and the hyperspectral image to overcome the uncertainty from water depth in the hyperspectral image and the limitation of spectral information in Lidar data. The texture data was added as a band in the hyperspectral image, that had 49 bands consisting of 48 spectral bands and 1 texture band. Fig.

이론/모형

Correction for the water surface reectance requires estimating the signal component that arises from reection of the water surface. In this study, the method of Hedley (2005) was used to estimate the reected energy from the water surface. The method assumes that the reectance of water is 0 or a minimum value in the near-infrared wavelength.
The airborne bathymetric Lidar data was preprocessed in the order of GPS/INS data process, laser data process, and topographic data classication (noise removal). Then the point cloud data was converted to raster format such as DEM (digital elevation model) using the cubic convolution interpolation method. Here, the resolution of the raster data had 1m which was the same resolution as the hyperspectral image.

성능/효과

with 160 validation samples (Table 1). Overall, 141 points among the 160 validation points corresponded to the detection result, indicating 88% accuracy. In the case of the rock, 80 validation points were detected as rock, indicating 100% detection rate.
Then, the two datasets were fused to increase detection accuracy. When compared with the results using individual data, fused data had 8%p and 17%p higher overall accuracy than Lidar and hyperspectral image data, respectively. Additionally, commission error was decreased 10-18%p and omission error is decreased 25%p.

후속연구

Second, there is a need to subdivide the classes of materials such as the sand, gravel, and mud along with the rock. Third, additional study is needed for the expansion of the geographic region. Because the Korean Peninsula is surrounded by three seas, and the characteristics of each sea are diverse.

참고문헌 (19)

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