Semi-automatic Building Area Extraction based on Improved Snake Model

개선된 스네이크 모텔에 기반한 반자동 건물 영역 추출

  • Park, Hyun-Ju (Department of Computer Engineering, Chonbuk National University) ;
  • Gwun, Ou-Bong (Department of Computer Engineering, Chonbuk National University)
  • Received : 2010.11.22
  • Accepted : 2011.01.07
  • Published : 2011.01.25

Abstract

Terrain, building location and area, and building shape information is in need of implementing 3D map. This paper proposes a method of extracting a building area by an improved semi-automatic snake algorithm. The method consists of 3-stage: pre-processing, initializing control points, and applying an improved snake algorithm. In the first stage, after transforming a satellite image to a gray image and detecting the approximate edge of the gray image, the method combines the gray image and the edge. In the second stage, the user looks for the center point of a building and the system sets the circular or rectangular initial control points by an procedural method. In the third stage, the enhanced snake algorithm extracts the building area. In particular, this paper sets the one tenn of the snake in a new way in order to use the proposed method for specializing building area extraction. Finally, this paper evaluated the performance of the proposed method using sky view satellite image and it showed that the matching percentage to the exact building area is 75%.

3차원 지도(3D Map)를 구축하기 위해서는 지형정보와 지도상에서 건물 영역 및 건물 형상 정보가 필요하다. 이를 위해 본 논문에서는 개선된 스네이크(Snake) 알고리즘으로 건물 영역을 반자동으로 추출하는 방법을 제안한다. 본 방법은 전처리, 제어점의 초기화, 개선된 스네이크 알고리즘 적용 세 단계로 구성한다. 첫 번째 단계에서는 위생영상을 그레이 영상으로 변환 후 근사 에지를 추출하여 그레이 영상과 합성한다. 두 번째 단계에서는 사용자가 건물의 중심점을 설정한 후 원형 또는 사각형 모양의 초기 제어점을 계산하여 설정한다. 세번째 단계에서는 개선된 스네이크 알고리즘을 적용하여 건물영역을 추출한다. 이러한 과정에서 스네이크 에너지 계산식의 한 항을 새로운 방법으로 설정하여 건물영역 추출용으로 특화하였다. 그리고 스카이 뷰의 위성영상을 이용하여 제안된 방법을 건물영역 매칭율을 평가하였는데 75%의 매칭율을 보였다.

Keywords

References

  1. Gulch, E., Digital Systems for automated cartographic feature extraction. International Archieves of Photogrammetry and Remote Sensing and Spatial Information Sciences, Vol. XXXIII, Part B2, pp. 241-255, 2000.
  2. Mayunga, S. D., Zhang, Y. and Coleman, D. J., Semi-Automatic Building Extraction Utilizing Quickbird Imagery, In Stilla, U., Rottensteiner, F. and Hinz, S. (Eds.), IAPRS, Vol. XXXVI, Part 3/W24, pp. 131-136, 2005.
  3. Florent Lafarge, Xavier Descombes, Josiane Zerubia and Marc Pierrot-Deseilligny, Automatic building extraction from DEMs using an object approach and application to the 3D-city modeling, ISPRS Journal of Photogrammetry and Remote Sensing, 63(3), 365-381, 2008.
  4. Beril Sırmacek, Student Member, IEEE, and Cem Unsalan, Member, IEEE, Urban-Area and Building Detection Using SIFT Keypoints and Graph Theory, IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, VOL.47, NO. 4, APRIL 2009.
  5. Noronha, S., Nevatia, R., Detection and modeling of buildings from multiple aerial images. IEEE Trans. Pattern Anal. Machine Intell. 23 (5),pp 501-518, 2001. https://doi.org/10.1109/34.922708
  6. McKeown, D.M., Bulwinkle, T., Cochran, S., Harvey, W., McGlone, C., Shufelt, J.A., Performance evaluation for automatic feature extraction. Int. Arch. Photogrammetry Remote Sensing 33 (Part B2), 379-394, 2000.
  7. Neuenschwander, W., Fua, P., Sze'kely, G., Kubler, O., From Ziplock snakes to VelcroTM surfaces. Automatic Extraction of Man-Made Objects from Aerial and Space Images, Birkhauser Verlag, pp. 105-114, 1995.
  8. LAU BEE THENG, CHOO AI LING, GANN Snake For Object Extractions From High Resolution Satellite Imagery, ICACTE, DOI 10.1109, 1005-1009, 2008.
  9. Lau Bee Theng, Member, IAENG, Automatic Building Extraction from Satellite Imagery, Engineering Letters, 13:3, EL_13_3_5, 2006.
  10. http://opencv.willowgarage.com/wiki