Double U-Net (W-Net): A change detection network with two heads for remote sensing imagery

Recently, the deep learning algorithms have been increasingly utilized in remote sensing change detection. However, incomplete buildings and the blurred edges caused by the complex scenes in change detection applications make the detection results fail to describe the real land cover changes. Superpixels can be used to alleviate edge blurring, but the existing superpixel methods cannot be trained jointly with the models in change detection. In this work, we investigated an innovative double-head method using deep learning, called double UNet (W-Net), which consists of a superpixel module and a change detection module. Due to the superpixel module, W-Net can handle building edges very well. In order to solve problem that multiple subtasks fail to achieve the optimal results, a two-branch multi-task coupling framework of change detection and superpixels is designed for W-Net, which enables the model to achieve a globally optimal detection performance. The advancement of the W-Net was demonstrated using three public datasets. The F1score on LEVIR-CD dataset was 0.9031 and kappa coefficient was 0.8969. The F1-score on WHU building dataset was 0.9172 and kappa coefficient was 0.9142. The F1-score on SYSU-CD dataset was 0.8167and and kappa coefficient was 0.7724. The experiments confirmed that the W-Net is capable to detect the edges of changed area better and outperforms the other advanced change detection methods.