Abstract
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 the building probability map can be derived by a state-of-the-art deep learning approach. In the second step, a decision fusion based fusion model refines and fuses the building changes from satellite stereo imagery and the digital surface models (DSMs). In the last step, the building probability maps are further fused with the building change indicators to generate an improved change detection result. Experiments on the multi-temporal data acquired over 5 years confirms the effectiveness of the proposed approach.
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