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Hurricane eye morphology extraction from SAR images by texture analysis

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Abstract

Tropical hurricanes are among the most devastating hazards on Earth. Knowledge about its intense inner-core structure and dynamics will improve hurricane forecasts and advisories. The precise morphological parameters extracted from high-resolution spaceborne Synthetic Aperture Radar (SAR) images, can play an essential role in further exploring and monitoring hurricane dynamics, especially when hurricanes undergo amplification, shearing, eyewall replacements and so forth. Moreover, these parameters can help to build guidelines for wind calibration of the more abundant, but lower resolution scatterometer wind data, thus better linking scatterometer wind fields to hurricane categories. In this paper, we develop a new method for automatically extracting the hurricane eyes from C-band SAR data by constructing Gray Level-Gradient Co-occurrence Matrices (GLGCMs). The hurricane eyewall is determined with a two-dimensional vector, generated by maximizing the class entropy of the hurricane eye region in GLGCM. The results indicate that when the hurricane is weak, or the eyewall is not closed, the hurricane eye extracted with this automatic method still agrees with what is observed visually, and it preserves the texture characteristics of the original image. As compared to Du’s wavelet analysis method and other morphological analysis methods, the approach developed here has reduced artefacts due to factors like hurricane size and has lower programming complexity. In summary, the proposed method provides a new and elegant choice for hurricane eye morphology extraction.

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Acknowledgements

The author acknowledges ESA for the S-1 data obtained through the SHOC campaign. We thank Gerd-Jan van Zadelhoff for support in using the Sentinel-1 data and Jur Vogelzang for providing suggestions for this paper. This research is partially supported by the National Key Research and Development Program of China (No. 2018YFC1406206). This research is partially supported by the National Natural Science Foundation of China (Grant No. 61802424). Ad Stoffelen is supported by the EUMETSAT OSI SAF.

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Authors and Affiliations

  1. Royal Netherlands Meteorological Institute, De Bilt, 3731GA, The Netherlands

    Weicheng Ni & Ad Stoffelen

  2. College of Meteorology and Oceanography, National University of Defense Technology, Changsha, 410073, China

    Weicheng Ni & Kaijun Ren

Authors
  1. Weicheng Ni
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  2. Ad Stoffelen
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  3. Kaijun Ren
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Contributions

Weicheng Ni received the B.S. Degree in geophysical information science from Zhejiang University, China in 2017, and is now working toward the Ph.D Degree with the National University of Defense Technology, Changsha, China.

He is currently a visiting scholar in the Royal Netherlands Meteorological Institute, De Bilt, The Netherlands, and is working on the inter-calibration between scatterometer data and Synthetic Aperture Radar data.

Ad Stoffelen received the M.Sc. Degree in physics from the Technical University of Eindhoven, The Netherlands, in 1987, and the Ph.D Degree in meteorology on scatterometry from the University of Utrecht, The Netherlands in 1998.

He currently leads a group on active satellite sensing with the Royal Netherlands Meteorological Institute, De Bilt, The Netherlands, and is involved in topics from future missions and retrieval to 24/7 operations, user training, and services. He is also deeply involved in the European Space Agency ADM-Aeolus Doppler Wind Lidar mission.

Kaijun Ren received the B.S. Degree in applied mathematics and the M.S. and Ph.D Degrees in computer science from the National University of Defense Technology, China in 1998, 2003, and 2008, respectively.

He is currently a Professor in the College of Meteorology and Oceanography and the College of Computer, National University of Defense Technology. His current research interests include highperformance computing, cloud computing, big data, and their interdisciplinary applications in ocean science and meteorology areas.

Corresponding author

Correspondence to Kaijun Ren.

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Ni, W., Stoffelen, A. & Ren, K. Hurricane eye morphology extraction from SAR images by texture analysis. Front. Earth Sci. 16, 190–205 (2022). https://doi.org/10.1007/s11707-021-0886-9

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