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Updated: Sep 10, 2025

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Sea Surface Wind Speed Retrieval from Marine Radar Image Sequences Based on GLCM-Derived Texture Features.

Hui Wang1, Haiyang Qiu1, Lei Wang2

  • 1School of Low-Altitude Equipment and Intelligent Control, Guangzhou Maritime University, Guangzhou 510725, China.

Entropy (Basel, Switzerland)
|August 28, 2025
PubMed
Summary

This study introduces a new method for measuring sea surface wind speed using X-band marine radar and texture features. The novel approach improves accuracy and applicability compared to traditional models.

Keywords:
GLCM-derived featuresX-band marine radarenergy valueentropy valuewind speed

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Area of Science:

  • Marine Meteorology
  • Remote Sensing
  • Oceanography

Background:

  • Sea surface wind speed is crucial for marine safety and offshore operations.
  • Existing radar wind speed retrieval methods lack adaptability and accuracy.
  • Empirical models struggle with generalization and precision.

Purpose of the Study:

  • To develop a novel sea surface wind speed retrieval method using X-band marine radar.
  • To leverage Gray-Level Co-occurrence Matrix (GLCM) texture features for improved accuracy.
  • To enhance the environmental adaptability and applicability of wind speed estimation.

Main Methods:

  • Utilized X-band marine radar image sequences for wind speed retrieval.
  • Applied a three-stage preprocessing pipeline: noise suppression, geometric correction, and interpolation.
  • Extracted wind streaks and analyzed GLCM texture features (energy and entropy).
  • Developed a segmented dual-parameter wind speed model based on identified features.

Main Results:

  • The proposed GLCM texture-based models significantly outperform traditional empirical models.
  • Energy and entropy models reduced mean errors by approximately 49.3% and 16.7%, respectively.
  • The energy stable model achieved a high correlation coefficient (0.89); the entropy model excelled at low wind speeds.

Conclusions:

  • The novel GLCM texture-based method offers a more accurate and efficient solution for sea surface wind speed retrieval.
  • The segmented dual-parameter model enhances robustness across varying environmental conditions.
  • This approach provides a valuable advancement for marine meteorology and related applications.