Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Development and Evaluation of a Texture-Based Urban Change Detection Method Using Very High Resolution SAR Imagery

고해상도 SAR 영상을 활용한 텍스처 기반의 도심지 변화탐지 기법 개발 및 평가

  • Kang, Ah-Reum (Satellite Information Promotion Team, Satellite Information Center, Korea Aerospace Research Institute(KARI)) ;
  • Byun, Young-Gi (Satellite Information Promotion Team, Satellite Information Center, Korea Aerospace Research Institute(KARI)) ;
  • Chae, Tae-Byeong (Satellite Information Promotion Team, Satellite Information Center, Korea Aerospace Research Institute(KARI))
  • 강아름 (항공우주연구원 위성정보활용센터 위성활용진흥팀) ;
  • 변영기 (항공우주연구원 위성정보활용센터 위성활용진흥팀) ;
  • 채태병 (항공우주연구원 위성정보활용센터 위성활용진흥팀)
  • Received : 2015.04.04
  • Accepted : 2015.06.23
  • Published : 2015.06.30
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Abstract

Very high resolution (VHR) satellite imagery provide valuable information on urban change monitoring due to multi-temporal observation over large areas. Recently, there has been increased interest in the urban change detection technique using VHR Synthetic Aperture Radar (SAR) imaging system, because it can take images regardless of solar illumination and weather condition. In this paper, we proposed a texture-based urban change detection method using the VHR SAR texture features generated from Gray-Level Co-Occurrence Matrix (GLCM). In order to evaluate the efficiency of the proposed method, the result was compared, visually and quantitatively, with the result of Non-Coherent Change Detection (NCCD) which is widely used for the change detection of VHR SAR image. The experimental results showed the greater detection accuracy and the visually satisfactory result compared with the NCCD method. In conclusion, the proposed method has shown a great potential for the extraction of urban change information from VHR SAR imagery.

고해상도 위성영상은 실시간으로 정확한 지표 상태에 대한 정보를 수집할 수 있어 도심지 모니터링에 효율적인 수단으로 사용되고 있다. 고해상도 Synthetic Aperture Radar (SAR) 영상은 기상상태와 태양고도의 제약을 받지 않고 영상을 취득할 수 있는 장점을 가지기 때문에 최근 이들 데이터를 활용한 도심지 변화탐지 기술에 대한 관심이 증대되고 있다. 본 연구에서는 Gray-Level Co-Occurrence Matrix (GLCM)을 통한 텍스처 정보추출과 이들 특징 정보를 통합적으로 활용하는 새로운 텍스처 기반의 SAR 변화탐지 기술을 제안하였다. 제안기법의 효용성을 평가하기 위해 기존의 SAR 영상 변화탐지를 위해 많이 사용된 Non-Coherent Change Detection (NCCD) 기법과의 시각적/정량적 비교평가를 수행하였다. 실험결과 제안기법이 보다 높은 변화탐지 정확도를 보였으며 시각적으로도 우수한 결과를 도출하였다. 결과적으로 제안된 변화탐지 방법은 고해상도 SAR 위성영상을 이용한 도심지 변화정보 추출에 유용하게 적용될 수 있으리라 판단된다.

Keywords

References

  1. Celik, T., 2010. Change Detection in Satellite Images Using a Genetic Algorithm Approach, IEEE Geoscience and Remote Sensing, 7(2): 386-390. https://doi.org/10.1109/LGRS.2009.2037024
  2. Dellinger, F., J. Delon, Y. Gousseau, J. Michel, and F. Tupin, 2014. Change detection for high resolution satellite images, based on SIFT descriptors and an a contrario approach, IGARSS, Quebec City, QC, July. 13-18, pp. 1281-1284.
  3. Dong, Y., B. Forster, and C. Ticehurst, 1997. Radar backscatter analysis for urban environment, International Journal of Remote Sensing, 18(6): 1351-1364. https://doi.org/10.1080/014311697218467
  4. Gong, M., Y. Li, L. Jiao, M. Jia, and L. Su, 2014. SAR change detection based on intensity and texture changes, ISPRS Journal of Photogrammetry and Remote Sensing, 93: 123-135. https://doi.org/10.1016/j.isprsjprs.2014.04.010
  5. Grey, W.M.F., A.J. Luckman, and D. Holland, 2003. Mapping urban change in the UK using satellite radar interferometry. Remote Sensing of Environment, 87(1): 16-22. https://doi.org/10.1016/S0034-4257(03)00142-1
  6. He, C., Y. Zhao, and A. Wei, 2010. Land-use/landcover change detection by using the extended change-vector analysis, Information Science and Engineering(ICISE), Hangzhou, China, Dec. 4-6, pp.3809-3812.
  7. Huang, S.Q., D.Z. Liu, X.H. Cai, 2009. A New Change Detection Algorithm for SAR Images, Proc.of APSAR, Xian Shanxi, Oct. 26-30, pp. 729-732.
  8. Jung, M.H., 2012. Early Disaster Damage Assessment Using Remotely Sensing Imagery:Damage Detection, Mapping and Estimation, The Institute of Electronics And Information Engineers, 49(2): 143-148.
  9. Kandaswamy, U., D.A. Adjeroh, and M.C. Lee, 2005. Efficient texture analysis of SAR imagery, IEEE Geoscience and Remote Sensing, 43(9): 2075-2083. https://doi.org/10.1109/TGRS.2005.852768
  10. Kim, G.H., S.P. Choi, W.S. Yook, and H.G. Sohn, 2005. Extraction of Urban Boundary Using Grey Level Co-Occurrence Matrix Method in Pancromatic satellite Imagery, Korean Society of Civil Engineers, 26(1) : 211-217.
  11. Lee, W.K., 2015. Development of Change Detection Algorithm using High Resolution SAR Image, KARI Satellite Information Application Project, Korea.
  12. Liao, M., L. Jiang, H. Lin, and D. Li, 2005. Urban change detection using coherence and intensity characteristics of multi-temporal ERS-1/2 Imagery, Fringe ATSR Workshop.
  13. Liu, W. and F. Yamazaki, 2011. Urban monitoring and change detection of central tokyo using high-resolution X-band SAR images, Proc. of IGARSS, Vancouver, BC, July. 24-29, pp.2133-2136.
  14. Matsuoka, M. and F. Yamazaki, 2004. Use of satellite SAR intensity imagery for detecting building areas damaged due to earthquakes, Earthquake Spectra, 20(3): 975-994. https://doi.org/10.1193/1.1774182
  15. Moser, G. and S.B. Serpico, 2006. Generalized minimum-error thresholding for unsupervised change detection from SAR amplitude imagery, IEEE Geoscience and Remote Sensing, 44(10): 2972-2982. https://doi.org/10.1109/TGRS.2006.876288
  16. Otsu, N., 1979. A Threshold Selection Method from Gray-Level Histograms, IEEE Trans. Sys., Man., Cyber. 9(1): 62-66. https://doi.org/10.1109/TSMC.1979.4310076
  17. Rignot, E.M. and J.J. Van Zyl, 1993. Change detection techniques for ERS-1 SAR data, IEEE Geoscience and Remote Sensing, 31(4): 896-906. https://doi.org/10.1109/36.239913
  18. Sirmacek, B. and C. Unsalan, 2009. Urban-Area and Building Detection Using SIFT Keypoints and Graph Theory, IEEE Geoscience and Remote Sensing, 47(4): 1156-1167. https://doi.org/10.1109/TGRS.2008.2008440
  19. Ulaby, F.T., F. Kouyate, B. Brisco, and T.L. Williams, 1986. Textural information in SAR images, IEEE Geoscience and Remote Sensing, 24(2): 235-245.

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