Comparison of Two Fusion Based Building Change Detection Methods Using Satellite Stereo Imagery and Dsms

Digital Surface Models (DSMs) can assist building change detection in a variety of approaches. Limited to the quality of DSMs from satellite stereo imagery, it is hard to reach precise change detection results using only DSMs. Therefore, DSMs should be used in combination with the spectral information from original stereo imagery. For that purpose, two fusion based methods, one using feature level fusion and the other using decision level fusion are proposed in our previous research. In this paper, these two methods are further evaluated and compared based on two different data sets. One test site features a typical urban environment which is captured by two pairs of very high resolution stereo imagery (IKONOS and WorldView-2). The other test site is located in an industrial area, the corresponding stereo imagery of the two dates are both from Cartosat-1. Quantitative and qualitative experiment results obtained from each dataset are analyzed in detail. Over all, the proposed feature fusion model give better results for the industrial area, while the decision fusion method works much better for the urban environment based on very high resolution imagery.