A supervised method for object-based 3D building change detection on aerial stereo images
There is a great demand for studying the changes of buildings over time. The current trend for building change detection combines the orthophoto and DSM (Digital Surface Models). The pixel-based change detection methods are very sensitive to the quality of the images and DSMs, while the object-based...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2014-08-01
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| Series: | The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences |
| Online Access: | http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-3/259/2014/isprsarchives-XL-3-259-2014.pdf |
| Summary: | There is a great demand for studying the changes of buildings over time. The current trend for building change detection combines the
orthophoto and DSM (Digital Surface Models). The pixel-based change detection methods are very sensitive to the quality of the images
and DSMs, while the object-based methods are more robust towards these problems. In this paper, we propose a supervised method for
building change detection. After a segment-based SVM (Support Vector Machine) classification with features extracted from the
orthophoto and DSM, we focus on the detection of the building changes of different periods by measuring their height and texture
differences, as well as their shapes. A decision tree analysis is used to assess the probability of change for each building segment and the
traffic lighting system is used to indicate the status "change", "non-change" and "uncertain change" for building segments. The proposed
method is applied to scanned aerial photos of the city of Zurich in 2002 and 2007, and the results have demonstrated that our method is
able to achieve high detection accuracy. |
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| ISSN: | 1682-1750 2194-9034 |