SUDANet: A Siamese UNet with Dense Attention Mechanism for Remote Sensing Image Change Detection

Change detection is one of the main applications of remote sensing images. Pixel-to-pixel change detection using deep learning has been a hot research spot. However, the current approach are not effective enough to fuse deep semantic features and raw spatial information, and the network does not have the ability to perform long-distance information aggregation due to the limitation of the convolutional kernel size. In this manuscript, we propose a Siamese UNet with a dense attention mechanism, named SUDANet to do change detection for remote sensing images. SUDANet add a channel attention mechanism and a self-attention mechanism to the dense skip connection between encoder and decoder which enable the model to fuse feature information in channel dimensions and spatial dimensions. Graph attention module is also added at the end of the encoder, enabling the model to perform correlation analysis and long-distance aggregation of deep semantic features. The experimental results on LEVIR dataset show that our method outperforms the state-of-the-art change detection methods.