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Extracting Hurricane Eye Morphology from Spaceborne SAR Images Using Morphological Analysis

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Hurricane Monitoring With Spaceborne Synthetic Aperture Radar

Part of the book series: Springer Natural Hazards ((SPRINGERNAT))

Abstract

Hurricanes are among the most destructive global natural disasters. Thus recognizing and extracting their morphology is important for understanding their dynamics. Conventional optical sensors, due to cloud cover associated with hurricanes, cannot reveal the intense air-sea interaction occurring at the sea surface. In contrast, the unique capabilities of spaceborne synthetic aperture radar (SAR) data for cloud penetration, and its backscattering signal characteristics enable the extraction of the sea surface roughness. Therefore, SAR images enable the measurement of the size and shape of hurricane eyes, which reveal their evolution and strength. In this study, using six SAR hurricane images, we have developed a mathematical morphology method for automatically extracting the hurricane eyes from C-band SAR data. Skeleton pruning based on discrete skeleton evolution (DSE) was used to ensure global and local preservation of the hurricane eye shape. This distance weighted algorithm applied in a hierarchical structure for extraction of the edges of the hurricane eyes, can effectively avoid segmentation errors by reducing redundant skeletons attributed to speckle noise along the edges of the hurricane eye. As a consequence, the skeleton pruning has been accomplished without deficiencies in the key hurricane eye skeletons. The subsequent reconstructed of the hurricane eyes thereby proves the morphology-based analyses results in a high degree of agreement with the hurricane eye areas derived from reference data based on NOAA manual work.

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Author information

Authors and Affiliations

  1. School of Civil & Environmental Engineering, The University of New South Wales, Sydney, NSW, Australia

    Isabella K. Lee, John C. Trinder & Zeyu Li

  2. Tarbiat Modares University, Tehran, Iran

    Ali Shamsoddini

  3. GST, National Oceanic and Atmospheric Administration (NOAA)/NESDIS, College Park, MD, 20740, USA

    Xiaofeng Li

Authors
  1. Isabella K. Lee
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  2. Ali Shamsoddini
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  3. Xiaofeng Li
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  4. John C. Trinder
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  5. Zeyu Li
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Correspondence to Xiaofeng Li .

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Editors and Affiliations

  1. National Oceanic and Atmospheric Administration, College Park, Maryland, USA

    Xiaofeng Li

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Lee, I.K., Shamsoddini, A., Li, X., Trinder, J.C., Li, Z. (2017). Extracting Hurricane Eye Morphology from Spaceborne SAR Images Using Morphological Analysis. In: Li, X. (eds) Hurricane Monitoring With Spaceborne Synthetic Aperture Radar. Springer Natural Hazards. Springer, Singapore. https://doi.org/10.1007/978-981-10-2893-9_7

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