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A Fine-Grained Semantic Alignment Method Specific to Aggregate Multi-Scale Information for Cross-Modal Remote Sensing

Fuzhong Zheng1, Xu Wang1, Luyao Wang1

  • 1College of Information and Communication, National University of Defense Technology, Wuhan 430074, China.

Sensors (Basel, Switzerland)
|October 28, 2023
PubMed
Summary

This study introduces a fine-grained semantic alignment method (FAAMI) for remote sensing image retrieval. FAAMI effectively aggregates multi-scale information and enhances semantic alignment between images and text, improving retrieval accuracy.

Keywords:
cross-modal retrievalfine-grained semantic alignmentmulti-scaleremote sensing

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Area of Science:

  • Computer Science
  • Remote Sensing
  • Artificial Intelligence

Background:

  • The increasing volume of remote sensing imagery necessitates efficient cross-modal retrieval.
  • Existing methods struggle with multi-scale features and semantic alignment between images and text.
  • A gap exists in comprehensively understanding the relationship between multi-scale targets and textual semantics.

Purpose of the Study:

  • To develop a fine-grained semantic alignment method (FAAMI) for remote sensing image retrieval.
  • To effectively aggregate multi-scale information from remote sensing images.
  • To improve the semantic understanding and cross-modal retrieval accuracy.

Main Methods:

  • Constructing multi-scale image features using a cross-layer feature connection.
  • Enhancing feature consistency with an efficient module to address semantic discrimination.
  • Employing a shallow cross-attention network for fine-grained semantic relationship capture between image regions and text.

Main Results:

  • FAAMI significantly outperforms state-of-the-art models on RSICD and RSITMD datasets.
  • Demonstrated substantial improvements in R@K and other key evaluation metrics.
  • Achieved mR values of 23.18% and 35.99% on the respective datasets.

Conclusions:

  • The proposed FAAMI method effectively addresses the challenge of multi-scale features in remote sensing image retrieval.
  • FAAMI enhances the semantic alignment between multi-scale image regions and textual descriptions.
  • The method offers superior performance and accuracy for cross-modal retrieval in remote sensing applications.