Journal of Korean Society for Geospatial Information Science (대한공간정보학회지)

Korea Spatial Information Society (대한공간정보학회)
권호 표지
DOI QR코드

DOI QR Code

Impervious Surface Mapping of Cheongju by Using RapidEye Satellite Imagery

RapidEye 위성영상을 이용한 청주시의 불투수면지도 생성기법

  • Park, Hong Lyun (Satellite Remote Sensing Lab, Korea Polar Research Institute, KIOST) ;
  • Choi, Jae Wan (School of Civil Engineering, Chungbuk National University) ;
  • Choi, Seok Keun (School of Civil Engineering, Chungbuk National University)
  • 박홍련 (한국해양과학기술원 부설 극지연구소 극지원격탐사연구실) ;
  • 최재완 (충북대학교 공과대학 토목공학부) ;
  • 최석근 (충북대학교 공과대학 토목공학부)
  • Received : 2014.02.05
  • Accepted : 2014.03.11
  • Published : 2014.03.31
Download PDF
⟨ Previous Next ⟩

Abstract

Most researches have created the impervious surface map by using low-spatial-resolution satellite imagery and are inefficient to generate the object-based impervious map with a broad area. In this study, segment-based impervious surface mapping algorithm is proposed using the RapidEye satellite imagery in order to map impervious area. At first, additional bands are generated by using TOA reflectance conversion RapidEye data. And then, shadow and water class are extracted using training data of converted reflectance image. Object-based impervious surface can be generated by spectral mixture analysis based on land cover map of Ministry of Environment with medium scale, in the case of other classes except shadow and water classes. The experiment shows that result by our method represents high classification accuracy compared to reference data, quantitatively.

많은 연구들은 저해상도 위성영상을 이용하여 불투수면을 생성하며, 광역적인 객체 단위의 불투수면을 생성하는 데에 효율적인 성과를 이루지 못하고 있는 실정이다. 본 논문에서는 RapidEye 위성영상을 활용한 객체 기반의 불투수면 생성 기법을 제안하였으며, 이를 실험지역에 적용하고자 하였다. 분광반사율로 변환된 RapidEye 위성영상을 활용하여 추가적인 밴드를 생성하였으며, 훈련자료를 이용하여 그림자 및 수계 클래스를 추출하였다. 해당 클래스를 제외한 나머지 클래스들은 환경부의 중분류 토지피복지도와 분광혼합분석 모델을 활용하여 피복단위의 불투수 비율 영상을 생성하였다. 참조자료와의 정량적 비교평가를 통하여 본 연구에서 적용한 불투수면 생성 방법의 효용성을 검증하였다.

Keywords

References

  1. Bernstein, L. S., Jin, X., Gregor, B. and Adler-Golden, S. M., 2012, Quick atmospheric correction code: algorithm description and recent upgrades, Optical Engineering, Vol. 51, No. 11, pp. 1-11.
  2. Cablk, M. E. and Minor, T. B., 2003, Detecting and discriminating impervious cover with high-resolution IKONOS data using principal component analysis and morphological operators, International Journal of Remote Sensing Vol. 24 No. 23, pp. 4627-4645. https://doi.org/10.1080/0143116031000102539
  3. Chang, C. I. and Heinz, D. C., 2000, Constrained subpixel target detection for remotely sensed imagery, IEEE Transactions on Geoscience and Remote Sensing, Vol. 38, No. 3, pp. 1144-1159. https://doi.org/10.1109/36.843007
  4. Cho, H. and Jeong, J., 2005, Estimating impervious surface fraction of Tanchon watershed using spectral mixture analysis, Korean Journal of Remote Sensing, Vol. 21, No. 6, pp. 457-468. https://doi.org/10.7780/kjrs.2005.21.6.457
  5. Hese, S., Lindner, M., Voltersen, M. and Berger, C., 2011, TerraSAR-X and RapidEye data for the parameterisation of relational characteristics of urban ATKIS DLM objects, International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XXXVIII-4/W19.
  6. Kim, S., Heo, J., Yun K. and Sohn, H., 2007, Impervious surface estimation using Landsat-7 ETM+ image in An-sung Area, Korean Journal of Remote Sensing, Vol. 23, No. 6, pp. 529-536. https://doi.org/10.7780/kjrs.2007.23.6.529
  7. Kim, S., Heo, J., Heo, J. and Kim, S., 2008, Impervious surface estimation of Jungnangcheon basin using satellite remote sensing and classification and regression tree, Journal of the Korean Society of Civil Engineers, Vol. 28, No. 6D, pp. 915-922.
  8. Lu, D., Weng, Q. and Li, G., 2006, Residential population estimation using a remote sensing derived impervious surface approach, International Journal of Remote Sensing Vol. 27 No. 16, pp. 3553-3570. https://doi.org/10.1080/01431160600617202
  9. Lu, D. and Weng, Q., 2009, Extraction of urban impervious surfaces from an IKONOS image, International Journal of Remote Sensing Vol. 30 No. 5, pp. 1297-1311. https://doi.org/10.1080/01431160802508985
  10. Park, H., Lee, J., Cho, Y., Choi, J. and Choi, S., 2013, A study on method of impervious surface using rapideye image and land cover map in Cheong-ju area, 2013 Conference of The Korean Society for GeoSpatial Information System, pp. 95-96.
  11. RapidEye AG., 2010, RapidEye Image Product Frequently Asked Question (FAQs), v.1.0, pp. 6-7.
  12. Sakong, H., 2003, An empirical study on analysis method of impervious surface using IKONOS image, The Journal of Geographic Information System Association of Korea, Vol. 11, No. 4, pp. 198-208.
  13. Tooke, T. R., Coops, N. C., Goodwin, N. R. and Voogt, J. A., 2009, Extracting urban vegetation characteristics using spectral mixture analysis and decision tree classifications, Remote Sensing of Environment, Vol. 113, pp. 398-407. https://doi.org/10.1016/j.rse.2008.10.005
  14. Zhang, X., Zhong, T., Wang, K. and Cheng, Z., 2009, Scaling of impervious surface area and vegetation as indicators to urban land surface temperature using satellite data, International Journal of Remote Sensing Vol. 30 No. 4, pp. 841-859. https://doi.org/10.1080/01431160802395219

Cited by

  1. Unsupervised Classification of Landsat-8 OLI Satellite Imagery Based on Iterative Spectral Mixture Model vol.22, pp.4, 2014, https://doi.org/10.7319/kogsis.2014.22.4.053
  2. 농림위성 활용 수종분류 가능성 평가를 위한 래피드아이 영상 기반 시험 분석 vol.37, pp.2, 2014, https://doi.org/10.7780/kjrs.2021.37.2.9