Intelligent-BCD: A Novel Knowledge-Transfer Building Change Detection Framework for High-Resolution Remote Sensing Imagery

Abstract

In the study of human social development and ecological environment monitoring, building change detection (BCD) is essential. Rapid and accurate BCD in complicated scenes and multiview high-resolution (HR) remote sensing images have garnered a lot of attention with the continual increase of image resolution and diverse satellite imaging modes. However, conventional BCD methods almost exclusively focus on the performance under specific, standard datasets and do not consider the robustness under off-nadir imaging and complex scene conditions. To address these issues, we propose an adaptative knowledge-driven multilevel attention BCD framework, called intelligent-BCD, in which we innovatively deploy four attention mechanisms. The compatibility design of the channel and spatial attention mechanism enables the model to fully mine deep features and meet BCD tasks in multiple imaging modes and complex scenes. Meanwhile, we propose a weight pool concept, a knowledge accumulation way of modeling attention adaptive. The model parameters obtained from the training of different data are stored to form the weight pool, and the weight for transfer application is reused in the new BCD task. Furthermore, we propose a new loss function, the dynamic domain loss function, that successfully addresses the problem of sample imbalance while motivating the model to pay greater attention to challenging sample learning. The benchmark experimental results for the four datasets, namely LEVIR-CD, LEVIR-CD+, WHU Building Dataset, and S2Looking, show that intelligent-BCD is superior to the competing methods in quantitative and qualitative evaluation. The transfer experiment in MtS-WH dataset demonstrates that intelligent-BCD has an excellent generalization and robustness.

Keywords

• Attention
• building change detection (BCD)
• knowledge-driven
• remote sensing
• transfer-learning