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Siamese change detection based on information interaction and fusion network.

Yanni Zhang1, Lei Yang1, Caigen Zhou1

  • 1School of Artificial Intelligence, Yancheng Teachers University, Yancheng, 224000, China.

Scientific Reports
|August 10, 2025
PubMed
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This study introduces a novel network for change detection, improving how intermediate features are handled and temporal relationships are modeled. The proposed methods enhance semantic information and network performance for applications like disaster monitoring.

Area of Science:

  • Remote Sensing
  • Computer Vision
  • Artificial Intelligence

Background:

  • Change detection is crucial for applications like disaster monitoring, analyzing differences in images over time.
  • Existing methods struggle with intermediate feature constraints and comprehensive temporal relationship modeling.
  • Simplistic fusion mechanisms in current approaches lead to suboptimal network performance.

Purpose of the Study:

  • To propose a novel network for change detection that addresses limitations in current methods.
  • To enhance semantic information and model bi-temporal relationships more effectively.
  • To improve the constraint of intermediate features using contrastive learning.

Main Methods:

  • A Feature Information Interaction Module (FIIM) utilizing spatial attention to boost semantic information.
Keywords:
Change detectionContrastive learningFeature fusionInformation interaction

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  • A Feature Pair Fusion Module (FPFM) employing a dual-branch structure for bi-temporal relationship modeling.
  • A Multi-Scale Supervision Method (MSSM) incorporating contrastive learning for intermediate feature constraint.

    • Main Results:

    • The proposed network demonstrated superior performance compared to state-of-the-art methods on benchmark datasets (CDD and LEVIR-CD).
    • The FIIM, FPFM, and MSSM modules effectively enhanced feature representation and temporal modeling.
    • Experimental results validate the effectiveness of the proposed approach in change detection tasks.

    Conclusions:

    • The developed network offers significant improvements in change detection accuracy and feature representation.
    • The proposed modules provide a robust framework for handling complex temporal dependencies in image data.
    • This work advances the field of change detection, particularly for time-series image analysis and monitoring applications.