A model-based approach for automatic building database updating from high-resolution space imagery
This article presents an approach for automatic building database updating from high-resolution space imagery based on the support vector machine (SVM) classification and building models. The developed approach relies on an idea that the buildings are similar in shape within an urban
block or a neighbourhood unit. First, the building patches are detected through classification of the pan-sharpened high-resolution space imagery along with the normalized digital surface model (nDSM) and the normalized difference vegetation index (NDVI) using the binary SVM classifier. Then,
the buildings that exist in the vector database but not seen in the image are detected through the analyses of the detected building patches. The buildings, which were constructed after the compilation date of the existing vector database, are extracted through the proposed model-based approach
that utilizes the existing building database as a building model library. The approach was implemented in selected urban areas of the Batikent district of Ankara, the capital city of Turkey, using the IKONOS images and the existing building database. The results validated the success of the
developed approach, with the building extraction accuracy computed to be higher than 80%.
Document Type: Research Article
Affiliations: 1: Department of Space Sciences and Technologies, Faculty of Sciences,Akdeniz University, Antalya,07058, Turkey 2: Department of Geodesy and Photogrammetry, Faculty of Engineering,Hacettepe University, Ankara,06800, Turkey
Publication date: 10 July 2012
- Editorial Board
- Information for Authors
- Subscribe to this Title
- Ingenta Connect is not responsible for the content or availability of external websites
- Access Key
- Free content
- Partial Free content
- New content
- Open access content
- Partial Open access content
- Subscribed content
- Partial Subscribed content
- Free trial content