MAEANet: Multiscale Attention and Edge-Aware Siamese Network for Building Change Detection in High-Resolution Remote Sensing Images

In recent years, using deep learning for large area building change detection has proven to be very efficient. However, the current methods for pixel-wise building change detection still have some limitations, such as a lack of robustness to false-positive changes and confusion about the boundary of dense buildings. To address these problems, a novel deep learning method called multiscale attention and edge-aware Siamese network (MAEANet) is proposed. The principal idea is to integrate both multiscale discriminative and edge structure information to improve the quality of prediction results. To effectively extract multiscale discriminative features, we design a contour channel attention module (CCAM) that highlights the edge of the changed region and combine it with the classical convolutional block attention module (CBAM) to construct multiscale attention (MA) module, which mainly contains channel, spatial and contour attention mechanisms. Meanwhile, to consider the structure information of buildings, we introduce the edge-aware (EA) module, which combines discriminative features with edge structure features to alleviate edge confusion in dense buildings. We conducted the experiments using LEVIR-CD and BCDD datasets. The proposed MA and EA modules can improve the F1-Score of the basic architecture by 1.13% on the LEVIR CD and by 1.39% on the BCDD with an accepted computation overhead. The experimental results demonstrate that the proposed MAEANet is effective and outperforms other state-of-the-art methods concerning metrics and visualization