Difference Guided VHR Remote Sensing Image Change Detection

VHR remote sensing images have abundant ground features and details, but it is a great challenge for machine understanding. The "same object with different spectral" problem caused by environment changes, such as seasonal alternation, bad weather and shadow, is the biggest challenge in multitemporal image change detection, which is more prominent in VHR images. For this problem, a novel difference guided VHR image change detection (DGCD) method is proposed in this paper. In the feature learning stage of DGCD model, difference features are used to guide the feature extraction to suit with the change detection task. In order to make the model focus on the change features, both of the spatial and channel attention mechanism are introduced. Finally, for the edge region of VHR image which is hard to be discriminated, a new edge enhanced loss function based on BCL loss is designed. Experiments on public datasets show the superiority of proposed DGCD method. It has good generalization ability in different classical challenging scenarios. Compared with the representative methods in recent years, the proposed DGCD method performs better on VHR image change detection.