RDP-Net: Region Detail Preserving Network for Change Detection

Change detection (CD) is an essential earth observation technique. It captures the dynamic information of land objects. With the rise of deep learning, convolutional neural networks (CNNs) have shown great potential in CD. However, current CNN models introduce backbone architectures that lose detailed information during learning. Moreover, current CNN models are heavy in parameters, which prevents their deployment on edge devices such as unmanned aerial vehicles (UAVs). In this work, we tackle this issue by proposing RDP-Net: a region detail preserving network for CD. We propose an efficient training strategy that constructs the training tasks during the warmup period of CNN training and lets the CNN learn from easy to hard. The training strategy enables CNN to learn more powerful features with fewer floating point operations (FLOPs) and achieve better performance. Next, we propose an effective edge loss that increases the penalty for errors on details and improves the network’s attention to details such as boundary regions and small areas. Furthermore, we provide a CNN model with a brand new backbone that achieves the state-of-the-art (SOTA) empirical performance in CD with only 1.70 M parameters. We hope our RDP-Net would benefit the practical CD applications on compact devices and could inspire more people to bring CD to a new level with the efficient training strategy. The code and models are publicly available at
https://github.com/Chnja/RDPNet
.