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Related Experiment Video

Updated: May 6, 2026

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Unsupervised domain adaptation for remote sensing semantic segmentation with the 2D discrete wavelet transform.

Junying Zeng1, Yajin Gu2, Chuanbo Qin3

  • 1School of Electronics and Information Engineering, Wuyi University, Guangdong, 529020, China.

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|October 9, 2024
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Summary

This study introduces a novel dual-space generative adversarial domain adaptation segmentation framework (DS-DWTGAN) to improve cross-domain segmentation for remote sensing images. The method enhances model stability and adaptability, outperforming state-of-the-art approaches.

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Area of Science:

  • Computer Vision
  • Remote Sensing
  • Machine Learning

Background:

  • Cross-domain segmentation in remote sensing is challenged by variations in spectra, scale, and resolution.
  • Existing domain adaptation methods often prioritize style over semantic information or overlook remote sensing specifics, leading to unstable training and biased results.

Purpose of the Study:

  • To propose a novel dual-space generative adversarial domain adaptation segmentation framework, DS-DWTGAN, to minimize domain differences.
  • To improve the performance and stability of semantic segmentation models across different remote sensing domains.

Main Methods:

  • Introduced a dual-space generative adversarial network incorporating a wavelet transform branch to capture frequency and semantic information.
  • Integrated output adaptation and data enhancement strategies for domain-invariant feature acquisition.
  • Leveraged wavelet transform to preserve semantic details in the frequency domain, mitigating conversion deviations.

Main Results:

  • Achieved superior performance on the PotsdamIRRG to Vaihingen task, with mIoU of 56.04% and mF1 of 67.28%.
  • Outperformed state-of-the-art methods by 2.81% (mIoU) and 2.08% (mF1).
  • Demonstrated enhanced model stability and reduced noise interference during domain migration.

Conclusions:

  • The proposed DS-DWTGAN framework effectively addresses challenges in cross-domain semantic segmentation for remote sensing images.
  • The wavelet transform integration and adaptation strategies significantly improve model adaptability and performance.
  • The method shows superior efficacy for unsupervised semantic segmentation of UAV remote sensing images compared to existing approaches.