Hurricane eye morphology extraction from SAR images by texture analysis

doi:10.1007/s11707-021-0886-9

Front. Earth Sci.

2022, Vol. 16

Issue (1) : 190-205 https://doi.org/10.1007/s11707-021-0886-9

RESEARCH ARTICLE

Hurricane eye morphology extraction from SAR images by texture analysis

Weicheng NI^1,², Ad STOFFELEN¹, Kaijun REN²(

)

¹. Royal Netherlands Meteorological Institute, De Bilt 3731GA, The Netherlands
². College of Meteorology and Oceanography, National University of Defense Technology, Changsha 410073, China

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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.

Keywords hurricane eyewall morphological parameter texture analysis Gray Level-Gradient Co-occurrence Matrix Two-dimensional Entropy Maximization

Corresponding Author(s): Kaijun REN

Online First Date: 01 June 2021 Issue Date: 04 March 2022

Cite this article:

Weicheng NI,Ad STOFFELEN,Kaijun REN. Hurricane eye morphology extraction from SAR images by texture analysis[J]. Front. Earth Sci., 2022, 16(1): 190-205.

URL:

https://academic.hep.com.cn/fesci/EN/10.1007/s11707-021-0886-9
https://academic.hep.com.cn/fesci/EN/Y2022/V16/I1/190

Fig.1 The number of typhoons landed in China between 2000 and 2017 according to the National Climate Center (NCC).

Tab.1 The Spatial Distribution of S1 SAR Data.

Fig.2 Hurricanes imaged by S1 C-band VH SAR. The hurricane names and the acquisition times are listed on top of the images. The bright spots represent land.

Tab.2 S1 SAR imagery information of hurricane events

Fig.3 (a) Original S1 EW image of the hurricane eye (Hurricane Lionrock, 27 August, 2016). (b) A general depiction of GLGCM. It can be divided into four quadrants with practical meaning using the gray level and gradient thresholds (s,t). (c) The GLGCM of the Hurricane Lionrock image, black lines indicate the estimated optimal thresholds (s,t). (d) Class A-D area of Hurricane Lionrock classified by two-dimensional entropy maximization method. X shape mark indicates the initial hurricane center. (e) Classification results of Hurricane Lionrock image (denoised by a PPB filter first) with the same method in (d). (f) Hurricane eye area extracted from (d), the hurricane center (shown as a white cross mark) is identified as the average position of the hurricane eye area.

Fig.4 The hurricane eyewall detection process. It is performed simultaneously in the clockwise and anti-clockwise directions. The white cross mark indicates the hurricane center location and the blue point the starting point. The red pixels indicate the hurricane eyewall by quadrant B components.

Fig.5 Distances between the starting points (the blue point in Fig. 4 for instance) and the hurricane centers (the white cross mark in Fig. 3(f) for instance).

Fig.6 The searches of the hurricane eyewall pixels. The yellow

∇

mark indicates the hurricane center. The fitted reference elliptical hurricane eyewall and BT hurricane center location (orange

Δ

mark) are added (see text).

Fig.7 (a −p) indicates the hurricane eyewall detection results. The left panel represents the original sub-image and the right panel the detected hurricane eyewall. The identified hurricane centers are shown in the right panel, as well as the temporally interpolated centers from BT data. No BT data found for (p).

Fig.8 (a) Positions of SAR extracted centers relative to BT interpolated centers. BT interpolated centers are used as origin, and the horizontal and vertical axes indicate eastward and northward directions. (b) The number of dots in eight polar angle intervals.

Fig.9 (a) Positions of SAR extracted centers relative to BT interpolated centers relative to the Best Track Direction. The origin is the BT interpolated centers. (b) The number of dots in eight polar angle intervals.

Fig.10 Processing flow chart of hurricane eye morphology extraction and the comparison of the characteristics of two methods.

Fig.11 (a) Hurricane eye retrieved with Du’s wavelet analysis method (Hurricane Megi, 29 September, 2016). (b) Hurricane eyewall retrieved with the proposed method.

Fig.12 (a) Hurricane eye retrieved with Du’s wavelet analysis method (Hurricane Gaston, 26 August, 2016). (b) Hurricane eyewall retrieved with the proposed method.

Tab.3 Hurricane morphological parameters estimated with the GLGCM (G) method and Du’s Wavelet Analysis method (WA) for closed hurricane eyewalls

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