Time-Series 3D Building Change Detection Based on Belief Functions

International audienceOne of the challenges of remote sensing image based building change detection is distinguishing building changes from other types of land cover alterations. Height information can be a great assistance for this task but its performance is limited to the quality of the height. Yet, the standard automatic methods for this task are still lacking. We propose a very high resolution stereo series data based building change detection approach that focuses on the use of time series information. In the first step, belief functions are explored to fuse the change features from the 2D and height maps to obtain an initial change detection result. In the second step, the building probability maps (BPMs) from the series data are adopted to refine the change detection results based on Dempster-Shafer theory. The final step is to fuse the series building change detection results in order to obtain a final change map. The advantages of the proposed approach are demonstrated by testing it on a set of time series data captured in North Korea