Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3, 259-264, 2014
http://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XL-3/259/2014/
doi:10.5194/isprsarchives-XL-3-259-2014
© Author(s) 2014. This work is distributed
under the Creative Commons Attribution 3.0 License.
 
11 Aug 2014
A supervised method for object-based 3D building change detection on aerial stereo images
R. Qin and A. Gruen Singapore ETH Centre, Future Cities Laboratory, 1 CREATE Way, #06-01 CREATE Tower, Singapore 138602, Singapore
Keywords: Change detection, Aerial images, Digital Surface Models, Support Vector Machine Abstract. 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.
Conference paper (PDF, 956 KB)


Citation: Qin, R. and Gruen, A.: A supervised method for object-based 3D building change detection on aerial stereo images, Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XL-3, 259-264, doi:10.5194/isprsarchives-XL-3-259-2014, 2014.

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