Wednesday 25 Jan 2017
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Application of the method for unsupervised change detection to the complete city of Dongying

In order to apply the methodology of unsupervised change detection on the complete city extent of Dongying, building geometries were delineated area-wide. The city comprises a total of around 30.000 buildings in 2013, whereas the number of changed buildings since 2006 was determined based on multi-temporal QuickBird and WorldView imagery by means of change detection.

After preprocessing of the imagery, clustering was applied to the multi-dimensional feature space of the object-based image difference vector in order to classify changed buildings according to Leichtle et al. (2017). A comprehensive number of around 7000 buildings were classified visually to serve as a control data set. In terms of accuracy, we achieved an overall accuracy of 87% and kappa statistics of 0,73.

Fig. 1 shows an excerpt of the classification result for the Eastern part of Dongying while Fig. 2 shows part of Western Dongying. Overall, the change detection workflow detected around 12.500 changed and 17.500 unchanged buildings within the city.

 

 

Fig.1: Result of change detection in the Eastern part of Dongying

 

 

Fig. 2: Result of change detection in the Western part of Dongying

 

 

Reference: Leichtle, T., Geiß, C., Wurm, M., Lakes, T., Taubenboeck, H. (2017). Unsupervised change detection in VHR remote sensing imagery – an object-based clustering approach in a dynamic urban environment. International Journal of Applied Earth Observation and Geoinformation. 54. pp.15-27.

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