Building change detection based on deep learning and belief function

This paper proposes a new approach for building change detection using multi-temporal satellite stereo data. This approach is composed of three main steps. Firstly building probably map can be derived based on the state-of-the-art deep learning approach. In the second step, a decision fusion based fusion model is proposed to highlight the building changes from satellite stereo imagery and the digital surface models (DSMs). In the last step, the building probability maps are used in the change fusion model. Experiments on the multi-temporal data acquired over 5 years confirms the effectiveness of the proposed approach