SegUNet: leveraging pretrained image embeddings for advanced water body segmentation

Abstract

Accurate segmentation of water bodies in satellite imagery is essential for environmental monitoring, resource management, and disaster response. This study introduces a novel deep learning framework that leverages image embeddings from a pre-trained SegFormer-B4 encoder (MiT-B4) without fine-tuning, combined with a U-Net decoder, to achieve precise water body segmentation in Sentinel-2 satellite imagery. Unlike conventional methods that rely on full fine-tuning, this approach significantly reduces computational cost and training time while maintaining high segmentation accuracy. The extracted transformer-based embeddings capture both local and global spatial features, serving as input to a lightweight U-Net decoder that efficiently reconstructs segmentation masks. Experimental results demonstrate that the proposed method outperforms the fine-tuned SegFormer-B4 model in training and testing scenarios, achieving superior Intersection over Union (IoU), F1 Score, Precision, and Recall. Despite the absence of fine-tuning, the SegFormer-B4 encoder effectively extracts meaningful spatial representations, enabling accurate segmentation with minimal computational overhead. This work highlights the advantages of integrating pre-trained transformer embeddings with a dedicated segmentation decoder, offering a scalable and efficient solution for water body mapping in remote sensing applications.