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Related Experiment Videos

Frequency-Aware Refinement Network with Multi-Scale Fusion for Remote Sensing Change Detection.

Xu Zhang1, Yue Du2, Zeyu Zhang3

  • 1School of Digital Low-Altitude, Suzhou Polytechnic University, Suzhou 215104, China.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

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This study introduces FARNet, a novel network for remote sensing change detection. FARNet enhances accuracy by combining frequency and RGB domain information for precise land cover variation identification.

Area of Science:

  • Computer Vision
  • Remote Sensing
  • Geospatial Analysis

Background:

  • Remote sensing change detection (RSCD) compares bi-temporal images to identify land cover variations.
  • Conventional RSCD methods struggle with visually similar backgrounds in complex scenarios.
  • Reliance on RGB domain information limits the distinction between changed objects and background.

Purpose of the Study:

  • To develop an advanced network for accurate remote sensing change detection.
  • To overcome limitations of existing methods in complex land cover change scenarios.
  • To improve the precision and robustness of change detection using multi-domain features.

Main Methods:

  • Proposed a frequency-aware refinement network (FARNet) employing a coarse-to-fine strategy.
Keywords:
change detectioncoarse-to-finefrequency-aware refinementmulti-scaleremote sensing

Related Experiment Videos

  • Introduced a frequency-aware module (FAM) for coarse localization using frequency domain information.
  • Developed a refinement fusion module (RFM) to leverage RGB details for precise boundary refinement, incorporating edge loss.

    • Main Results:

    • FARNet effectively identifies blurred boundaries of changed objects resembling background.
    • The network refines segmentation boundaries using high-resolution RGB details for precise detection.
    • Experiments show FARNet significantly outperforms existing methods in accuracy and robustness.

    Conclusions:

    • FARNet offers a superior approach to remote sensing change detection in complex environments.
    • The integration of frequency and RGB domain information enhances detection precision.
    • The proposed method achieves state-of-the-art performance on benchmark datasets.