DLB-CNet: Difference Learning-Based Convolution Network for Building Change Detection

Abstract

Change detection (CD) in remote sensing (RS) images is a technique used to analyze and characterize surface changes from remotely sensed data at different time periods. However, current deep-learning-based methods sometimes struggle with the diversity of targets in complex RS scenarios, leading to issues, such as false detections and loss of detail. To address these challenges, we propose a method called difference learning-based convolution and network (DLB-CNet) for building CD (BCD). In DLB-CNet, we use difference learning module (DLM), accomplishing the extraction of building change features by enhancing the feature differences between the two images and enhancing model robustness. Additionally, an innovative attention module called integration attention (IA) is introduced to efficiently process semantic information by jointly focusing on global representation subspaces. Our model achieves impressive results on the LEVIR-CD dataset, WHU-CD dataset, and CDD dataset, with ${F}1$ -scores of 90.56%, 92.28%, and 94.98%, respectively, demonstrating its superiority over the state-of-the-art methods.