Re-Net: Multibranch Network With Structural Reparameterization for Landslide Detection in Optical Imagery

Deep learning techniques have been widely adopted in landslide detection by offering powerful feature extraction capabilities and automated processes. However, the pursuit of higher accuracy has led to increasingly complex network structures, which limits the efficiency of models in landslide detection. To tackle this challenge, we have developed a dynamic module, called five-branch feature extraction module (FFEM), based on the theory of structural reparameterization. This module is designed to reconstruct the encoder of the U-shaped network. Our novel network, Re-Net, effectively integrates information from multiple scales during training by utilizing its multibranch structure, which is facilitated by the FFEM. During inference, leveraging the structural reparameterization, the FFEM in Re-Net transforms as a convolutional layer, achieving an impressive 52.9% reduction in parameters, and 34.98% reduction in floating point operations. The efficiency improvement of Re-Net does not come at the expense of sacrificing landslide recognition accuracy. In the public dataset (Bijie dataset), Re-Net achieved improvements of 2.81% in intersection over union (IoU) and 1.93% in F1-Score. In postearthquake landslide detection tasks (Luding Dataset), Re-Net exhibited respective improvements of 2.29% and 1.52%. Moreover, in the task of Landslide Detection, Re-Net demonstrates superior segmentation accuracy compared to other convolutional neural networks, such as Unet++. When compared to other reparameterization modules, FFEM shows significant improvements in IoU and F1-Score in the Bijie dataset, with an average increase of 0.65% and 0.45%, respectively. Similarly, in the Luding dataset, FFEM demonstrates average improvements of 1.56% in IoU and 1.04% in F1-Score.