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Spatial-Frequency Decoupling Alignment Encoding for Remote Sensing Change Detection.

Xu Zhang1, Yue Du2, Weiran Zhou3

  • 1School of Electronics and Information Engineering, Suzhou Polytechnic University, Suzhou 215104, China.

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|March 28, 2026
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Summary
This summary is machine-generated.

A new spatial-frequency decoupling alignment encoding (SDA-Encoding) method improves remote sensing change detection by better capturing complex targets and subtle textures. This approach enhances boundary distinction between change targets and backgrounds.

Keywords:
dual-domain alignment fusionmulti-scale high-frequency interactionposition-aware low-frequency enhancementremote sensing change detectionselective fusion attention

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Area of Science:

  • Remote Sensing
  • Computer Vision
  • Signal Processing

Background:

  • Existing remote sensing change detection methods struggle with complex targets and subtle textures.
  • Accurate boundary detection between change targets and background remains a challenge.

Purpose of the Study:

  • To propose a novel method, spatial-frequency decoupling alignment encoding (SDA-Encoding), for improved remote sensing change detection.
  • To effectively leverage both spatial and frequency domain information for enhanced detection accuracy.

Main Methods:

  • Utilized a Transformer encoder for bi-temporal feature extraction.
  • Applied wavelet transform to decouple features into low-frequency and high-frequency components.
  • Developed multi-scale high-frequency interaction (MHI) and position-aware low-frequency enhancement (PLE) modules, incorporating spatial pyramid pooling, dual-domain alignment fusion (DAF), coordinate attention, and selective fusion attention (SFA).

Main Results:

  • The SDA-Encoding method demonstrated significant performance improvements on three benchmark datasets.
  • The approach successfully achieved unified detection of both fine-grained and structural changes.
  • Enhanced ability to distinguish change target boundaries from the background was observed.

Conclusions:

  • SDA-Encoding offers a robust solution for remote sensing change detection by effectively integrating spatial and frequency domain information.
  • The proposed method overcomes limitations of existing techniques in handling complex targets and subtle textural differences.
  • Future work may explore further optimizations and applications of this dual-domain approach.