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Urban Area Building Reconstruction Using High Resolution SAR Image

고해상도 SAR 영상을 이용한 도심지 건물 재구성

  • Kang, Ah-Reum (Department of Geoinformation Engineering, Sejong University) ;
  • Lee, Seung-Kuk (Department of Geoinformation Engineering, Sejong University) ;
  • Kim, Sang-Wan (Department of Geoinformation Engineering, Sejong University)
  • 강아름 (세종대학교 공간정보공학과) ;
  • 이승국 (세종대학교 공간정보공학과) ;
  • 김상완 (세종대학교 공간정보공학과)
  • Received : 2013.06.10
  • Accepted : 2013.06.15
  • Published : 2013.08.30

Abstract

The monitoring of urban area, target detection and building reconstruction have been actively studied and investigated since high resolution X-band SAR images could be acquired by airborne and/or satellite SAR systems. This paper describes an efficient approach to reconstruct artificial structures (e.g. apartment, building and house) in urban area using high resolution X-band SAR images. Building footprint was first extracted from 1:25,000 digital topographic map and then a corner line of building was detected by an automatic detecting algorithm. With SAR amplitude images, an initial building height was calculated by the length of layover estimated using KS-test (Kolmogorov-Smirnov test) from the corner line. The interferometric SAR phases were simulated depending on SAR geometry and changable building heights ranging from -10 m to +10 m of the initial building height. With an interferogram from real SAR data set, the simulation results were compared using the method of the phase consistency. One of results can be finally defined as the reconstructed building height. The developed algorithm was applied to repeat-pass TerraSAR-X spotlight mode data set over an apartment complex in Daejeon city, Korea. The final building heights were validated against reference heights extracted from LiDAR DSM, with an RMSE (Root Mean Square Error) of about 1~2m.

공간해상도 약 1 m의 고해상도 X-band SAR 위성이 이용되면서 SAR를 이용한 도심지 모니터링, 표적탐지, 건물 재구성에 관한 연구가 진행되고 있다. 본 연구에서는 고해상도 TerraSAR-X SAR 영상을 이용한 도심지 건물 재구성을 수행하였다. 도심지 건물 재구성을 위하여 1:25,000 수치지형도로부터 건물의 외곽선을 추출하였으며, 추출한 건물의 외곽선을 기반으로 SAR 영상에서 모서리반사 위치를 찾았다. KS 테스트(Kolmogorov-Smirnov Test)에 기반하여 고해상도 SAR 진폭영상의 건물 모서리반사 위치로부터 레이오버 길이를 측정하여 건물의 초기 높이를 설정하였다. 진폭영상을 이용하여 추출한 건물의 초기 높이 기준 -10 m에서 +10 m로 건물의 높이를 변화시키며 도심지에 적합한 간섭위상 시뮬레이션을 수행하여 TerraSAR-X 간섭위상과의 위상 일치성 계산을 하였다. 위상 일치의 경향성 분석을 통해 건물의 높이를 설정해 줌으로써 고해상도 SAR 영상을 이용한 도심지 건물 재구성 연구를 진행하였다. 대전지역의 아파트 단지에 적용한 결과, 진폭영상과 간섭위상을 이용하여 추정된 건물 높이는 LiDAR로부터 추출된 높이를 기준으로 약 1~2 m 정도의 RMSE (Root Mean Square Error)를 보였다. 개발된 알고리즘은 향후 TerraSAR-X와 TanDEM-X 간섭쌍 자료에 적용할 경우, 보다 도심지 모니터링에 효과적으로 이용될 수 있을 것이다.

Keywords

References

  1. Ferretti, A., A. Fumagalli, N. Novali, F. Prati, C.F. Rocca, and A. Rucci, 2011. A new algorithm for processing interferometric data-stacks: SqueeSAR, IEEE Transactions on Geoscience and Remote Sensing, 49(9): 3460-3470. https://doi.org/10.1109/TGRS.2011.2124465
  2. Gao, G., L. Liu, L. Zhao, G. Shi, and G. Kuang, 2009. An Adaptive and Fast CFAR Algorithm Based on Automatic Censoring for Target Detection in High-Resolution SAR Images, IEEE transactions on geoscience and remote sensing, 47(6): 1685-1697. https://doi.org/10.1109/TGRS.2008.2006504
  3. Guida, R., A. Iodice, D. Riccio, and U. Stilla, 2008. Model-based interpretation of high-resolution SAR images of buildings, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1(2): 107-119. https://doi.org/10.1109/JSTARS.2008.2001155
  4. Liao, M., L. Jiang, H. Lin, B. Huang, and J. Gong, 2008. Urban Change Detection Based on Coherence and Intensity Characteristics of SAR Imagery, Photogrammetric Engineering and Remote Sensing, 74(8): 999-1006. https://doi.org/10.14358/PERS.74.8.999
  5. Massey, F.J., 1951. The Kolmogorov-Smirnov Test for Goodness of Fit, Journal of the American Statistical Association, 46(253): 68-78. https://doi.org/10.1080/01621459.1951.10500769
  6. Miller, L.H., 1956. Table of Percentage Points of Kolmogorov Statistics, Journal of the American Statistical Association, 51(273): 111-121. https://doi.org/10.1080/01621459.1956.10501314
  7. Soergel, U., U. Thoennessen, and U. Stilla, 2003a. Iterative building reconstruction in multi-aspect InSAR data, International Archives of Photogrammetry and Remote Sensing, 34: 186-192.
  8. Soergel, U., U. Thoennessen, and U. Stilla, 2003b. Reconstruction of buildings from interferometric SAR data of built-up area, International Archives of Photogrammetry and Remote Sensing, 34: 59-64.
  9. Soergel, U., K. Schulz, U. Thoennessen, and U. Stilla, 2005. Integration of 3D data in SAR mission planning and image interpretation in urban areas, Information Fusion, 6(4): 301-310. https://doi.org/10.1016/j.inffus.2004.06.007
  10. Thiele, A., E. Cadario, K. Schulz, U. Thoennessen, and U. Soergel, 2007. InSAR phase profiles at building locations, International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 36(3/W49A): 203-208.
  11. Thiele, A., E. Cadario, K. Schulz, U. Thoennessen, and U. Soergel, 2008. Building recognition from InSAR data by detail analysis of phase profiles. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences, 37(B3A): 191-196.
  12. Thiele, A., E. Cadario, K. Schulz, and U. Soergel, 2010a. Analysis of Gable-Roofed Building Signature in Multiaspect InSAR Data, IEEE Geoscience and Remote Sensing Letters, 7(1):83-87. https://doi.org/10.1109/LGRS.2009.2023476
  13. Thiele, A., S. Hinz, and E. Cadario, 2010b. Combining GIS and InSAR Data for 3D Building Reconstruction, Geoscience and Remote Sensing Symposium (IGARSS), Honolulu, HI, July. 25-July. 30, pp2418-2421.
  14. Yoon, G.W., S.W. Kim, Y.W. Lee, and D.C. Lee, 2011. High Resolution InSAR Phase Simulation using DSM in Urban Areas, Korean Journal of Remote Sensing, 27(2): 181-190.(in Korean with English abstract) https://doi.org/10.7780/kjrs.2011.27.2.181
  15. Zhou, H.X., 2003. The research of detecting ship target and ship wake from SAR imagery, Nat. Univ. Defense Technol., Changsha, China.