BUILDINGS CHANGE DETECTION USING HIGH-RESOLUTION REMOTE SENSING IMAGES WITH SELF-ATTENTION KNOWLEDGE DISTILLATION AND MULTISCALE CHANGE-AWARE MODULE

Abstract. Building change detection from remote sensing images is vital for many applications such as urban planning and dynamic monitoring, smart city construction, and geographical information census. In recent years, with the improvement of artificial intelligence and computer vision techniques, deep learning algorithms, especially convolutional neural networks (CNN), provide automatic detection and extraction methods. Unlike traditional approaches relying on shallow manual features, CNN can generate the deep semantic features by fusing spatial and spectral information, which is conducive to identifying building change regions. However, most deep CNN models directly fuse different level features and recover spatial details, which probably introduce redundant background information and noise from shallow layers. Considering the building’s multiscale, convolution operation with fixed receptive fields cannot obtain a strong global feature response to changed regions. To address the above problems, we develop a CNN framework for automatic building region change detection using dual-temporal high-resolution remote sensing images. To refine the shallow features, self-attention knowledge distillation strategies are introduced to FCN. Furthermore, we propose the multiscale feature change-aware module to integrate the globally changed information in different decoders. Finally, the model aggregates the multi-scale regional change information and outputs the prediction map. The results of comparative visual analysis and quantitative evaluation in two public datasets demonstrate that the proposed network model can improve the accuracy and efficiency of the automatic building change detection (85.69 IoU, 97.56 OA on the WHU dataset, and 83.72 IoU, 97.64 OA on the LEVIR-CD dataset).