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Adaptive frequency collaboration for remote sensing change detection.

Feng Zhou1, Xinyu Zhang2, Hui Shuai3

  • 1School of Computer Science, Nanjing Audit University, Nanjing, 211815, China.

Neural Networks : the Official Journal of the International Neural Network Society
|October 24, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces an adaptive frequency collaboration network (AFCN) for improved remote sensing change detection. By separating frequency components, AFCN enhances accuracy in identifying land cover changes.

Keywords:
Change detectionDeep learningFrequency disentanglementRemote sensing

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Area of Science:

  • Remote Sensing
  • Geospatial Analysis
  • Computer Vision

Background:

  • Deep learning methods for remote sensing change detection often aggregate frequency components, hindering performance.
  • High-frequency components capture fine details but can introduce spurious differences.
  • Low-frequency components preserve global structures, aiding in accurate change localization.

Purpose of the Study:

  • To propose an adaptive frequency collaboration network (AFCN) for improved remote sensing change detection.
  • To disentangle frequency components for distinct feature extraction.
  • To enhance change detection accuracy and detail preservation.

Main Methods:

  • Developed an adaptive frequency collaboration network (AFCN).
  • Designed a position-specific low-pass filter for adaptive low-frequency component extraction.
  • Obtained high-frequency components by subtracting low-frequency parts, utilizing wavelet reconstruction principles.
  • Incorporated an auxiliary edge detection task to enhance spatial details.

Main Results:

  • AFCN achieved state-of-the-art performance on benchmark datasets (LEVIR-CD, PX-CLCD, WHU-CD).
  • Achieved intersection over union (IoU) scores of 85.30%, 94.13%, and 90.03% respectively.
  • Demonstrated improved accuracy and detail preservation in change detection.

Conclusions:

  • The proposed AFCN effectively utilizes frequency information for superior remote sensing change detection.
  • Frequency disentanglement and auxiliary edge detection are crucial for accurate, detail-preserving results.
  • AFCN represents a significant advancement in change detection methodologies.