FDFF-Net: A Full-Scale Difference Feature Fusion Network for Change Detection in High-Resolution Remote Sensing Images
Feng Gu, Pengfeng Xiao, Xueliang Zhang, Zhenshi Li, Dilxat Muhtar
Abstract
Deep learning techniques have made significant advances in remote sensing change detection task. However, it remains a great challenge to detect the details of changed areas from high-resolution remote sensing images. In this study, we propose a full-scale differential feature fusion network (FDFF-Net) for change detection, which can alleviate pseudo-changes and reduce the loss of change details during detection. In the encoding stage, a dense difference fusion module is proposed to effectively mine and fuse the multiple difference for each feature level between bitemporal images, leading to a substantial reduction in missed detection of change areas. Additionally, the different levels of difference features are aggregated through a full-scale skip connection, allowing the network to detect multiple changed objects with various sizes. In the decoding stage, a strip space attention module is designed to enhance the perception of the change areas, which improves the ability to detect detailed changes. The experiments on three change detection datasets, CDD, LEVIR-CD, and S2Looking, demonstrate that FDFF-Net outperforms the compared state-of-the-art methods, and can detect more complete changes of small objects and clear contours of changed areas.