WaterSegformer: A lightweight model for water body information extraction from remote sensing images

Abstract

Accurate and efficient extraction of water body information from remote sensing images is of great help to monitor water resources at the macro level, natural disaster prediction, and water pollution detection and prevention. Although many large models have achieved extremely high accuracy in remote sensing image water segmentation tasks, lightweight models are still a non-negligible choice for many application scenarios because of the limitation of computing and storage resources. Here, WaterSegformer is described, an efficient and powerful lightweight water body segmentation model based on Segformer-b0. The Deepmask module is designed to make the model pay more attention to the details in the image and use Lovász loss to improve IoU. In addition, DeepLabv3+ is used as the teacher model to guide the training of the model in the way of relational knowledge distillation. WaterSegformer realizes 95.06% mIoU on the test set with only 6.38 G and 3.72 M of FLOPs and parameters, respectively. Experimental results show that WaterSegformer achieves an excellent balance between accuracy, computational complexity and model size, which is hardware-friendly, easy to deploy and enables real-time segmentation. This method provides a new idea for water body information extraction from remote sensing images in practical applications.