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Building Segmentation in Mountainous Environment Based on Improved Watershed Algorithm

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Published:25 February 2020Publication History

ABSTRACT

Making full use of satellite remote sensing technology to analyze and study high-resolution remote sensing images is helpful to the efficient supervision of urban land use. However, the segmentation and extraction of building boundary in mountainous area has always been a difficult problem to be solved. Therefore, it is particularly important to carry out the research on the segmentation and extraction of building boundary in mountainous area. In this paper, based on the traditional watershed algorithm, the gray image gradient operator and morpho-logical opening and closing operation are combined to improve the accuracy of image segmentation in mountainous area, and the experimental results are quantified by structural similarity to verify the improved algorithm Watershed algorithm is superior to traditional water-shed algorithm in building segmentation accuracy of high-resolution remote sensing image in multi mountainous area.

References

  1. Wang Xiaogang.Multi-scale remote sensing image segmentation algorithm based on water-shed transform [J]. Beijing Surveying and Mapping, 2019,33 (08): 886--890.Google ScholarGoogle Scholar
  2. E.Simonton, H.Oriot, R.Garello. Rectangular Building Extraction from Stereoscopic Airborne Radar Images[J]. IEEE Transactions on Geoscience and Remote Sensing. 2005, 43 (10): 2386--2395Google ScholarGoogle ScholarCross RefCross Ref
  3. Sun Xian, Wang Hongqi. Object-based Boosting Method for Automatic Extraction of Building Objects in High Resolution Remote Sensing Images [J]. Journal of Electronics & Information Technology, 2009, 31 (1): 177--181Google ScholarGoogle Scholar
  4. Zhou Yanan, Shen Zhanfeng, Luo Jiancheng, et al. Object-oriented urban building extrac-tion with shadow assistance [J]. Geography and Geographic Information Science, 2010, 26 (3): 37--40Google ScholarGoogle Scholar
  5. K. Karantzalos, N. Paragios. Recognition-Driven Two-Dimensional Competing Priors Toward Automatic and Accurate Building Detection[J]. IEEE Transaction on Geoscience and Remote Sensing, 2009, 47(1): 133--144Google ScholarGoogle ScholarCross RefCross Ref
  6. T. Yaakov, A. Amir. Automatic Segmentation of Moving Objects in Video Sequences: A Region Labeling Approach[J]. IEEE Transactions on Circuits and Systems for Video Technology, 2002, 12 (7): 597--612.Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. A. Sampath, J. Shan. Building Boundary Tracing and Regularization from Airborne Lidar Point Clouds[J]. Photogrammetric Engineering & Remote Sensing, 2007, 73(7):805--812Google ScholarGoogle ScholarCross RefCross Ref
  8. Wang Z, Bovik A C, Sheikh H R, et al. Image quality assessment: from error visibility to structural similarity[J]. IEEE Transactions on Image Processing, 2004, 13(4):600--612.Google ScholarGoogle ScholarDigital LibraryDigital Library

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    • Published in

      cover image ACM Other conferences
      ICVIP '19: Proceedings of the 3rd International Conference on Video and Image Processing
      December 2019
      270 pages
      ISBN:9781450376822
      DOI:10.1145/3376067

      Copyright © 2019 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 25 February 2020

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