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SRDFNet: Semantic Refinement and Differential Features for High-Resolution Change Detection.

Wenbo Zhao1, Donghua Lu1, Yingjun Zhao1

  • 1Beijing Research Institute of Uranium Geology, Beijing 100029, China.

Sensors (Basel, Switzerland)
|June 12, 2026
PubMed
Summary

This study introduces SRDFNet, a novel semantic change detection network that enhances accuracy by addressing class imbalance and variable object sizes. SRDFNet significantly improves performance over existing methods on benchmark datasets.

Keywords:
deep learningfeature refinementmulti-task learningremote sensingsemantic change detection

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Remote Sensing

Background:

  • Semantic change detection is crucial for monitoring environmental and urban transformations.
  • Existing methods struggle with class imbalance and variable object sizes, leading to misclassification and reduced accuracy.
  • BGSNet provides a foundational framework but requires enhancements for improved performance.

Purpose of the Study:

  • To propose SRDFNet (Semantic Refinement and Differential Features), a novel network for improved semantic change detection.
  • To address limitations in existing methods, specifically class imbalance and variable object sizes.
  • To enhance topological relationship perception and segmentation accuracy.

Main Methods:

  • Introduced a hierarchical graph module (HGM) for compacting multi-scale features into semantic graph nodes, utilizing graph attention for semantic interaction.
  • Developed a difference enhancement (DE) module with multi-receptive-field convolutions to extract difference information.
  • Implemented a semantic refine (SR) module for lightweight residual refinement of bi-temporal semantic features.

Main Results:

  • SRDFNet achieved state-of-the-art results on the SECOND and HRSCD datasets, outperforming BGSNet and other methods.
  • On the SECOND dataset, SRDFNet achieved 87.64% OA and 70.31% mIoU, with notable gains over BGSNet.
  • On the HRSCD dataset, SRDFNet achieved 98.13% OA and 52.67% mIoU, ranking first among evaluated methods.

Conclusions:

  • SRDFNet effectively mitigates issues of class imbalance and variable object sizes in semantic change detection.
  • The proposed HGM, DE, and SR modules contribute to enhanced topological perception and segmentation accuracy.
  • SRDFNet represents a significant advancement in semantic change detection accuracy and robustness.