Work

LU-Net: Lightweight U-Shaped Network for Water Body Extraction of Remote Sensing Images

Year: 2025

Type: article

Abstract: Deep learning-based water body extraction methods generally focus on maximizing accuracy while neglecting inference speed, which can make them challenging to apply in real-time applications. To address this problem, this paper proposes a lightweight u-shaped network (LU-Net), which improves inference speed while maintaining comparable accuracy. To reduce inference latency, a lightweight decoder block (LDB) is designed, which employs a depthwise separable convolution structure to accelerate the decoding process. To enhance accuracy, a lightweight convolutional block attention module (LCBAM) is designed, which effectively captures water-specific spectral and spatial characteristics through a dual-attention mechanism. To improve multi-scale water boundary extraction, a structurally re-parameterized multi-scale fusion prediction module (SRMFPM) is designed, which integrates multi-scale water boundary information through convolutions of different sizes. Comparative experiments are conducted on the GID and LoveDA datasets, with model performance assessed using the MIoU metric and inference latency. The results demonstrate that LU-Net achieves the lowest GPU latency of 3.1 MS and the second-lowest CPU latency of 36 MS in the experiments. On the GID, LU-Net achieves the MIoU of 91.36%, outperforming other tested methods. On the LoveDA datasets, LU-Net achieves the second-highest MIoU of 86.32% among the evaluated models, which is 0.08% lower than the top-performing CGNet. Considering both latency and MIoU, LU-Net demonstrates commendable efficiency on the GID and LoveDA datasets across all compared networks.

Source: Water

DOI: 10.3390/w17182763

Authors: Chengzhi Deng, Ruqiang He, Zhaoming Wu, Xiaowei Sun, Shengqian Wang

Institution: Jiangxi University of Water Resources and Electric Power

Language: English

Topic: Remote-Sensing Image Classification

Subfield: Media Technology

Field: Engineering

Domain: Physical Sciences

SDG: Clean water and sanitation

Open Access status: gold

Estimated APC paid: $3,137