The Study on Spatial Classification of Riverine Environment using UAV Hyperspectral Image

UAV를 활용한 초분광 영상의 하천공간특성 분류 연구

  • 김영주 (주식회사 자연과기술) ;
  • 한형준 (주식회사 네이처앤휴먼) ;
  • 강준구 (한국건설기술연구원 하천실험센터)
  • Received : 2018.08.10
  • Accepted : 2018.10.05
  • Published : 2018.10.31


High-resolution images using remote sensing (RS) is importance to secure for spatial classification depending on the characteristics of the complex and various factors that make up the river environment. The purpose of this study is to evaluate the accuracy of the classification results and to suggest the possibility of applying the high resolution hyperspectral images obtained by using the drone to perform spatial classification. Hyperspectral images obtained from study area were reduced the dimensionality with PCA and MNF transformation to remove effects of noise. Spatial classification was performed by supervised classifications such as MLC(Maximum Likelihood Classification), SVM(Support Vector Machine) and SAM(Spectral Angle Mapping). In overall, the highest classification accuracy was showed when the MLC supervised classification was used by MNF transformed image. However, it was confirmed that the misclassification was mainly found in the boundary of some classes including water body and the shadowing area. The results of this study can be used as basic data for remote sensing using drone and hyperspectral sensor, and it is expected that it can be applied to a wider range of river environments through the development of additional algorithms.


Classification;Drone;Hyperspectral image;River environment;UAV


Supported by : 한국연구재단


  1. J. J. Seo, "The Study on Land Cover Classification of Hyperspectral Image Using Decision Tree Method", Master thesis, Chonbuk National University, 2017.
  2. J. M. Kang, J. S. Lee, J. B. Kim, C. Zhang, "A Study to Compare SVM with Maximum Likelihood Classification Using the High Resolution Satellite Imagery", Proceedings of 35th Conference of Korean Society of Civil Engineers, pp.1563-1566, 2009.
  3. J. S. Park, W. H. Lee, M. H. Jo, "Improving Accuracy of Land Cover Classification in River Basins Using Landsat-8 OLI Image, Vegetation Index and Water Index", Journal of the Korean Association of Geographic Information Studies, Vol.19, No.2, pp.98-106. DOI:
  4. S. H, Kim, K. S. Lee, J. R. Ma, M. J. Kook, "Current Status of Hyperspectral Remote Sensing: Principle, Data Processing Techniques, and Applications", Korean Journal of Remote Sensing, Vol.21, No.4, pp.341-369, 2005.
  5. H. G. Cho, K. S. Lee, "Comparison between Hyperspectral and Multispectral Images for the Classification of Coniferous Species", Korean Journal of Remote Sensing, Vol.30, No.1, pp.25-36, 2014. DOI:
  6. H. L. Park, J. W. Choi, "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, Vol.25, No.1, pp.9-17, 2017. DOI:
  7. Y. J. Park, H. J. Jang, Y. S. Kim, K. H. Baik, H. S. Lee, "A Research on the Applicability of Water Quality Analysis using the Hyperspectral Sensor", Journal of the Korean Society for Environmental Analysis, Vol.17, No.3, pp.113-125, 2014.
  8. D. Stratoulias, H. Balzter, A. Zlinszky, V. R. Toth, "Assessment of ecophysiology of lake shore reed vegetation based on chlorophyll fluorescence, field spectroscopy and hyperspectral airborne imagery", Remote Sensing of Environment, Vol.157, pp.72-84, 2015. DOI:
  9. T. H. Song, "Development of Korean Typed Drone for Water Resources management", Water for Future, Vol.49, No.6, pp.30-36, 2016.
  10. F. E. Nicodemus, J. C. Richmond, J. J. Hsia, I. W. Ginsberg, T. Limperis, "Geometrical Considerations and Nomenclature for Reflectance", U.S. Department of Commerce, National Bureau of Standards, USA, 1977.
  11. S. Kaewpijit, J. Le Moigne, T. El-Ghazawi, "Automatic reduction of hyperspectral imagery using wavelet spectral analysis", IEEE Transactions on Geoscience and Remote Sensing, Vol.41, No.4, pp.863-871, 2003. DOI:
  12. D. Y. Han, Y. W. Cho, Y .I. Kim, Y. W. Lee, "Feature Selection for Image Classification of Hyperion Data", Korean Journal of Remote Sensing, Vol.19, No.2, pp.171-179, 2003.
  13. A. A. Green, M. Berman, P. Switzer, M. D. Craig, "A transformation for ordering multispectral data in terms of image quality with implications for noise removal", IEEE Transactions on Geoscience and Remote Sensing, Vol.26, No.1, pp.65-74, 1988. DOI:
  14. Q. S. Li, F. K. K. Wong, T. Fung, "Assessing the Utility of UAV-borne Hyperspectral Image and Photogrammetry Derived 3D Data for Wetland Species Distribution Quick Mapping", ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol.XLII-2, No.W6, pp.209-215, 2017. DOI:
  15. J. B. Campbell, R. H. Wynne, "Introduction to Remote Sensing, 5th Edition", The Guilford Press, USA, pp.684, 2011.
  16. G. Camps-Valls, L. Bruzzone, "Kernel-based methods for hyperspectral image classification", IEEE Transactions on Geoscience and Remote Sensing, Vol.43, No.6, pp.1351-1362, 2005. DOI:
  17. J. R. Jensen, "Introductory Digital Image Processing: A Remote Sensing Perspective, 4th Edition"(J. H. Im, H. G. Sohn. S. Park, Trans.), SIGMAPRESS, pp.397-401, 470-471, 477-478, 2016.
  18. J. R. Jensen, "Remote Sensing of the Environment: An Earth Resource"(H. S. Chae, Trans.), SIGMAPRESS, pp.402-403, 2002.