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The detection and monitoring of changes in urban buildings, as a major place for human activities, have been considered profoundly in the field of remote sensing. In recent years, comparing with other traditional methods, the deep learning-based methods have become the mainstream methods for urban building change detection due to their strong learning ability and robustness. Unfortunately, often, it is difficult and costly to obtain sufficient samples for the change detection method development. As a result, the application of the deep learning-based building change detection methods is limited in practice. In our work, we proposed a novel multi-task network based on the idea of transfer learning, which is less dependent on change detection samples by appropriately selecting high-dimensional features for sharing and a unique decoding module. Different from other multi-task change detection networks, with the help of a high-accuracy building mask, our network can fully utilize the prior information from building detection branches and further improve the change detection result through the proposed object-level refinement algorithm. To evaluate the proposed method, experiments on the publicly available WHU Building Change Dataset were conducted. The experimental results show that the proposed method achieves F1 values of 0.8939, 0.9037, and 0.9212, respectively, when 10%, 25%, and 50% of change detection training samples are used for network training under the same conditions, thus, outperforming other methods.

Built-Up Area Change Detection Using Multi-Task Network with Object-Level Refinement