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A Transformer-Based Network for Change Detection in Remote Sensing Using Multiscale Difference-Enhancement.

Gulinazi Ailimujiang1, Yiliyaer Jiaermuhamaiti1, Huxidan Jumahong1

  • 1College of Network Security and Information Technology, YiLi Normal University, Yining 835000, China.

Computational Intelligence and Neuroscience
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Summary

This study introduces TUNetCD, a novel transformer-based U-shaped network for remote sensing change detection. It effectively enhances multiscale features to improve accuracy and overcome noise challenges in bitemporal images.

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Area of Science:

  • Computer Science
  • Remote Sensing
  • Artificial Intelligence

Background:

  • Transformer-based methods show promise in change detection but struggle with noise-induced semantic object loss and incompleteness in bitemporal images.
  • Existing transformer approaches do not fully address these noise-related challenges in change detection.

Purpose of the Study:

  • To propose TUNetCD, a transformer-based multiscale difference-enhancement U-shaped network for robust change detection in remote sensing.
  • To enhance feature representation and mitigate noise effects for improved change detection accuracy.

Main Methods:

  • The proposed TUNetCD utilizes a multilayer Swin-Transformer encoder to extract multilevel feature maps.
  • A Swin-Transformer feature difference map processing module is employed to enhance these multilevel features.
  • A lightweight decoder is used to generate the final change map.

Main Results:

  • Comprehensive experiments were conducted on the LEVIR-CD and DSIFN-CD benchmark datasets.
  • TUNetCD demonstrated superior performance compared to other advanced transformer-based change detection methods.
  • The method effectively addresses issues of semantic object loss and incompleteness caused by noise.

Conclusions:

  • TUNetCD offers an effective solution for change detection in remote sensing, particularly in the presence of noisy bitemporal images.
  • The proposed multiscale difference-enhancement strategy significantly improves the performance of transformer-based change detection.