Feature Decomposition-Optimization-Reorganization Network for Building Change Detection in Remote Sensing Images

Building change detection plays an imperative role in urban construction and development. Although the deep neural network has achieved tremendous success in remote sensing image building change detection, it is still fraught with the problem of generating broken detection boundaries and separation of dense buildings, which tends to produce saw-tooth boundaries. In this work, we propose a feature decomposition-optimization-reorganization network for building change detection. The main contribution of the proposed network is that it performs change detection by respectively modeling the main body and edge features of buildings, which is based on the characteristics that the similarity between the main body pixels is strong but weak between the edge pixels. Firstly, we employ a siamese ResNet structure to extract dual-temporal multi-scale difference features on the original remote sensing images. Subsequently, a flow field is built to separate the main body and edge features. Thereafter, a feature optimization module is designed to refine the main body and edge features using the main body and edge ground truth. Finally, we reorganize the optimized main body and edge features to obtain the output results. These constitute a complete end-to-end building change detection framework. The publicly available building dataset LEVIR-CD is employed to evaluate the change detection performance of our network. The experimental results show that the proposed method can accurately identify the boundaries of changed buildings, and obtain better results compared with the current state-of-the-art methods based on the U-Net structure or by combining spatial-temporal attention mechanisms.