# LGFUNet: A Water Extraction Network in SAR Images Based on Multiscale Local Features with Global Information

## TL;DR

This paper introduces LGFUNet, a new deep learning model for extracting water bodies from SAR images, improving accuracy by combining global and local features.

## Contribution

The novel LGFUNet model integrates a Swin-Transformer, DECASPP, and LGFF modules to enhance water extraction from SAR images.

## Key findings

- The LGFUNet model outperforms existing methods like U-Net and Swin-UNet in water extraction accuracy.
- The model effectively reduces confusion between mountain shadows and water bodies.
- The LGFF module helps retain spatial details lost during downsampling.

## Abstract

To address existing issues in water extraction from SAR images based on deep learning, such as confusion between mountain shadows and water bodies and difficulty in extracting complex boundary details for continuous water bodies, the LGFUNet model is proposed. The LGFUNet model consists of three parts: the encoder–decoder, the DECASPP module, and the LGFF module. In the encoder–decoder, the Swin-Transformer module is used instead of convolution kernels for feature extraction, enhancing the learning of global information and improving the model’s ability to capture the spatial features of continuous water bodies. The DECASPP module is employed to extract and select multiscale features, focusing on complex water body boundary details. Additionally, a series of LGFF modules are inserted between the encoder and decoder to reduce the semantic gap between the encoder and decoder feature maps and the spatial information loss caused by the encoder’s downsampling process, improving the model’s ability to learn detailed information. Sentinel-1 SAR data from the Qinghai–Tibet Plateau region are selected, and the water extraction performance of the proposed LGFUNet model is compared with that of existing methods such as U-Net, Swin-UNet, and SCUNet++. The results show that the LGFUNet model achieves the best performance, respectively.