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http://dx.doi.org/10.17820/eri.2019.6.3.171

Correlation Analysis on the Water Depth and Peak Data Value of Hyperspectral Imagery  

Kang, Joongu (Department of Land, Water and Environment Research, Korea Institute of Civil Engineering and Building Technology)
Lee, Changhun (Nature and Technology Inc.)
Yeo, Hongkoo (Department of Land, Water and Environment Research, Korea Institute of Civil Engineering and Building Technology)
Kim, Jongtae (Nature and Technology Inc.)
Publication Information
Ecology and Resilient Infrastructure / v.6, no.3, 2019 , pp. 171-177 More about this Journal
Abstract
The hyperspectral images can be analyzed in more detail compared to the conventional multispectral images so they can be used for analyzing surface properties which are difficult to detect. Therefore, the purpose of this study is to obtain information on river environment by using actual depth data and drone-based images. For this purpose, this study acquired the image values for 100 points of 1 survey line using drone-based hyperspectral sensors and analyzed the correlation in comparison with the actual depth information obtained through ADCP. The ADCP measurements showed that the depth tended to get deeper toward the center and that the average water depth was 0.81 m. As a result of analyzing the hyperspectral images, the value of maximum intensity was 645 and the value of minimum intensity was 278, and the correlation between the actual depth and the results of analyzing the hyperspectral images showed that the depth increased as the value of maximum intensity decreased.
Keywords
Correlation coefficient; Drone; Hyperspectral; Peak data value; Water depth;
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Times Cited By KSCI : 1  (Citation Analysis)
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1 Behmann, J., Steinrücken, J. and Plümer, L. 2014. Detection of early plant stress responses in hyperspectral images. Journal of Photogrammetry and Remote Sensing 93: 98-111.   DOI
2 Choi, J.W., Hong, C.S., Shin, K.Y., Lee, J.U., Kim, J.A., Cho, Y.C. and Yu, S.J. 2018 Comparative analysis of ADCP flow measurement according to river bed material. Ecology and Resilient Infrastructure 5: 156-162. (in Korean)   DOI
3 Dierssen, H.M., Chlus, A. and Russell, B. 2015. Hyperspectral discrimination of floating mats of sea grass wrack and the macroalgae Sargassum in coastal waters of Greater Florida Bay using airborne remote sensing. Remote sensing of environment 167: 247-258.   DOI
4 Goetz, A.F.H. 1991. Imaging spectrometry for studying earth, air, fire and water. EARSeL Advances in Remote Sensing 1: 3-15.
5 Haest, M., Cudahy, T., Rodger, A., Laukamp, C., Martens, E. and Caccetta, M. 2013. Unmixing the effects of vegetation in airborne hyperspectral mineral maps over the Rocklea Dome iron-rich palaeochannel system (Western Australia). Remote Sensing of Environment 129: 17-31.   DOI
6 Heo, A., Choi, S., Lee, J.H., Kim, T. and Park, D.J. 2010. Optical system design and image processing for hyperspectral imaging systems. Journal of the Korea Institute of Military Science and Technology 13: 328-335. (in Korean)
7 Kim, S.H., Lee, K.S., Ma, J.R. and Kook, M.J. 2015. Current status of hyperspectral remote sensing: principle, data processing techniques, and applications. Korean journal of remote sensing 21: 341-369. (in Korean)
8 Kodikara, G.R., Woldai, T., Van Ruitenbeek, F.J., Kuria, Z., Van der Meer, F., Shepherd, K.D. and Van Hummel, G.J. 2012. Hyperspectral remote sensing of evaporate minerals and associated sediments in Lake Magadi area, Kenya. International Journal of Applied Earth Observation and Geoinformation 14: 22-32.   DOI
9 Landgrebe, D. 2002. Hyperspectral image data analysis. IEEE Signal Processing Magazine 35: 17-28.   DOI
10 Lausch, A., Heurich, M., Gordalla, D., Dobner, H.J., Gwillym-Margianto, S. and Salbach, C. 2013. Forecasting potential bark beetle outbreaks based on spruce forest vitality using hyperspectral remote-sensing techniques at different scales. Forest Ecology and Management 308: 76-89.   DOI
11 Lee, K.H. and Lee, S.H. 2012. Monitoring of floating green algae using ocean color satellite remote sensing. Journal of the Korean Association of Geographic Information Studies 15: 137-147. (in Korean)   DOI
12 Li, Q.S., Wong, F.K.K. and Fung, T. 2017. Assessing the utility of UAV-borne hyperspectral image and photogrammetry derived 3D data for wetland species distribution quick mapping. Remote Sensing and Spatial Information Sciences XLII-2: 209-215.
13 Mhanolakis, D. and Shaw, G. 2002. Detection algorithms for hyperspectral imaging applications. IEEE Signal Processing Magazine 35: 29-43.   DOI
14 Park, H.L. and Choi, J.W. 2017. Accuracy evaluation of supervised classification by using morphological attribute profiles and additional band of hyperspectral imagery. Journal of the Korean Society for Geo-Spatial Information Science 25: 9-17. (in Korean)   DOI
15 Park, Y.J., Jang, H.J., Kim, Y.S., Baik, K.H. and Lee, H.S. 2014. A research on the applicability of water quality analysis using the hyperspectral sensor. Journal of the Korean Society for Environmental Analysis 17: 113-125. (in Korean)
16 Seo, J.J. 2017. The Study on land cover classification of hyperspectral image using decision tree method. Master's thesis, Chonbuk University, Chonju. (in Korean)
17 Shaw, G.A. and Burke, H.K. 2003. Spectral imaging for remote sensing. Lincoln Laboratory Journal 14: 3-28.
18 Van der Meer, F. 2003. Bayesian inversion of imaging spectrometer data using a fuzzy geological outcrop model. International Journal of remote sensing 24: 4301-4310.   DOI
19 Shin, J.I. and Lee K.S. 2011. Development of target detection algorithm using spectral pattern observed from hyperspectral imagery. Journal of the Korea Institute of Military Science and Technology 14: 1073-1080. (in Korean)   DOI
20 Stratoulias, D., Balzter, H., Zlinszky, A. and Toth, V.R. 2014. Assessment of ecophysiology of lake shore reed vegetation based on chlorophyll fluorescence, filed spectroscopy and hyperspectral airborne imagery. Remote Sensing of Environment 157: 72-84.   DOI
21 Mhanolakis, D., Marden, D. and Shaw, G. 2003. Hyperspectral image processing for automatic target detection applications. Lincoln Laboratory Journal 14: 79-116.