Hierarchical Method of Urban Building Extraction Inspired by Human Perception

In a high-resolution satellite image, buildings can be considered as clustered objects belonging to the same category. Human perception of such objects consists of an initial identification of simple instances followed by recognition of more complicated ones by deduction. Inspired by this observation, a hierarchical building extraction framework is proposed to simulate the process, which includes three major components. First, a total variation-based segmentation algorithm is presented to decompose the given image into object-level elements. Then, a shape analysis is applied to extract some common and easily identified rectangular buildings. Finally, the detection of buildings with complex structures is formulated as a deduction problem based on preceding extracted information in terms of maximum a posteriori (MAP) estimation and a Bayesian based approach is proposed to deal with it. The experimental results demonstrate that the proposed framework is capable of efficiently identified urban buildings from high-resolution satellite images.

• Record URL:
  • http://digital.ipcprintservices.com/publication/?i=185745&p=&l=&m=&ver=&pp=
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/00991112
• Supplemental Notes:
  • Abstract used by permission of American Society for Photogrammetry and Remote Sensing.
• Authors:
  • Tao, Chao
  • Tan, Yihua
  • Zou, Zheng-rong
• Publication Date: 2013-12

Language

• English

Media Info

• Media Type: Print
• Features: Figures; Photos; References; Tables;
• Pagination: pp 1109-1119
• Serial:
  • Photogrammetric Engineering and Remote Sensing
  • Volume: 79
  • Issue Number: 12
  • Publisher: American Society for Photogrammetry and Remote Sensing
  • ISSN: 0099-1112

Subject/Index Terms

• TRT Terms: Artificial satellites; City planning; Detection and identification; Geographic information systems; Perception; Simulation; Structures; Urban areas
• Subject Areas: Data and Information Technology; Planning and Forecasting; Transportation (General); I72: Traffic and Transport Planning;

Filing Info

• Accession Number: 01504455
• Record Type: Publication
• Files: TRIS
• Created Date: Jan 24 2014 2:29PM