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Related Experiment Video

Updated: Aug 4, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
03:31

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Automatic extraction of surface water based on lightweight convolutional neural network.

Jikang Wan1, Bin Yong2

  • 1State Key Laboratory of Hydrology-Water Resources and Hydraulic Engineering, Hohai University, Nanjing 211100, China; Key Laboratory of Water Big Data Technology of Ministry of Water Resources, Hohai University, Nanjing 210098, China.

Ecotoxicology and Environmental Safety
|March 30, 2023
PubMed
Summary

This study introduces a new method for accurately extracting surface water from remote sensing images, even in challenging conditions like shadows. The developed lightweight neural network (EDCM) enables fast, large-area water mapping with over 95% accuracy.

Keywords:
AutomaticDeep learningGlobal mappingLightweight convolutional neural networkRemote sensingWater extraction

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Area of Science:

  • Remote Sensing
  • Hydrology
  • Artificial Intelligence

Background:

  • Accurate surface water extraction is crucial for global water cycle studies and water resource management.
  • Existing methods using multi-spectral remote sensing struggle with shadows, leading to inaccuracies and manual adjustments.
  • Shadows in images have spectral similarities to water, complicating traditional water extraction techniques.

Purpose of the Study:

  • To develop a fast, large-area, and automatic method for surface water extraction.
  • To improve water extraction accuracy in complex scenarios affected by shadows.
  • To overcome limitations of traditional index-based water extraction methods.

Main Methods:

  • Introduced thermal infrared band data for pre-treatment.
  • Proposed a lightweight neural network (EDCM) combining image classification and semantic segmentation models.
  • Employed multi-scale training of samples using lightweight convolutional networks to capture multi-scale context information.

Main Results:

  • The EDCM model achieved the highest accuracy in three highly heterogeneous test scenarios.
  • Achieved an accuracy exceeding 95.28% in all selected test areas.
  • Demonstrated superior performance compared to existing methods in complex environments.

Conclusions:

  • The EDCM model offers a robust solution for high-precision surface water extraction in challenging areas.
  • The integration of thermal infrared data and a lightweight neural network enhances automatic water mapping capabilities.
  • The proposed method addresses the limitations of traditional approaches, enabling efficient and accurate remote sensing monitoring of surface water.