Ecology and Resilient Infrastructure

Korean Society of Ecology and Infrastructure Engineering (응용생태공학회)
권호 표지
DOI QR코드

DOI QR Code

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

초분광 영상의 최대 강도값과 하천 수심의 상관성 분석

  • 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.)
  • 강준구 (한국건설기술연구원 국토보전연구본부) ;
  • 이창훈 (주식회사 자연과기술) ;
  • 여홍구 (한국건설기술연구원 국토보전연구본부) ;
  • 김종태 (주식회사 자연과기술)
  • Received : 2019.09.24
  • Accepted : 2019.09.26
  • Published : 2019.09.30
Download PDF
⟨ Previous Next ⟩

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.

초분광 영상은 기존 다중분광 영상에 비해 보다 세밀한 분석이 가능하며 감지가 어려운 지표 성질의 분석에 유용하게 활용될 수 있다. 따라서 본 연구에서는 수심에 대한 실측데이터와 드론 기반의 영상을 이용하여 하천환경 정보를 획득하는 것이 목적으로써 이를 위해 드론 기반의 초분광 센서를 활용하여 1개 측선 100개 지점에 대한 영상값을 취득하였으며 ADCP를 통해 확보된 실제 수심정보와 비교하여 상관관계를 분석하였다. ADCP 측정결과 중앙으로 갈수록 수심이 깊어지는 경향을 보이고 있으며 수심은 평균 0.81 m로 나타났다. 초분광 영상 분석 결과 최대 강도가 가장 높은 지점은 645, 가장 낮은 지점은 278이며 실제 수심과 초분광 영상분석결과의 상관성을 분석한 결과 최대 강도값이 감소할수록 수심은 증가하는 것으로 나타났다.

Keywords

References

  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. https://doi.org/10.1016/j.isprsjprs.2014.03.016
  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) https://doi.org/10.17820/ERI.2018.5.3.156
  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. https://doi.org/10.1016/j.rse.2015.01.027
  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. https://doi.org/10.1016/j.rse.2012.10.011
  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. https://doi.org/10.1016/j.jag.2011.08.009
  9. Landgrebe, D. 2002. Hyperspectral image data analysis. IEEE Signal Processing Magazine 35: 17-28. https://doi.org/10.1109/msp.2017.2766286
  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. https://doi.org/10.1016/j.foreco.2013.07.043
  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) https://doi.org/10.11108/kagis.2012.15.3.137
  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., Marden, D. and Shaw, G. 2003. Hyperspectral image processing for automatic target detection applications. Lincoln Laboratory Journal 14: 79-116.
  14. Mhanolakis, D. and Shaw, G. 2002. Detection algorithms for hyperspectral imaging applications. IEEE Signal Processing Magazine 35: 29-43. https://doi.org/10.1109/79.974724
  15. 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) https://doi.org/10.7319/kogsis.2017.25.1.009
  16. 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)
  17. 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)
  18. Shaw, G.A. and Burke, H.K. 2003. Spectral imaging for remote sensing. Lincoln Laboratory Journal 14: 3-28.
  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) https://doi.org/10.9766/KIMST.2011.14.6.1073
  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. https://doi.org/10.1016/j.rse.2014.05.021
  21. 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. https://doi.org/10.1080/0143116021000047929