Hurricane eye morphology extraction from SAR images by texture analysis

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.

Graphical abstract

Keywords

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

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 high-performance computing, cloud computing, big data, and their interdisciplinary applications in ocean science and meteorology areas.

References

[1] Belmonte R M, Stoffelen A (2019). Characterizing ERA-Interim and ERA5 surface wind biases using ASCAT. Ocean Sci, 15(3): 831–852

[2] Brink A D (1992). Thresholding of digital images using two-dimensional entropies. Pattern Recognit, 25(8): 803–808

[3] Cheng Y, Huang S, Liu A K, Ho C, Kuo N (2012). Observation of typhoon eyes on the sea surface using multi-sensors. Remote Sensing of Environment, 123(6): 434–442

[4] Chen S, Wu C, Chen D, Tan W (2009). Scene classification based on gray level-gradient co-occurrence matrix in the neighborhood of interest points. IEEE

[5] de Kloe J, Stoffelen A, Verhoef A (2017). Improved use of scatterometer measurements by using stress-equivalent reference winds. IEEE J Sel Top Appl Earth Obs Remote Sens, 10(5): 2340–2347

[6] Deledalle C A, Denis L, Tupin F (2009). Iterative weighted maximum likelihood denoising with probabilistic patch-based weights. IEEE Trans Image Process, 18(12): 2661–2672

[7] Du Y, Vachon P W (2003). Characterization of hurricane eyes in RADARSAT-1 images with wavelet analysis. Can J Remote Sens, 29: 491–498

[8] Du Y, Vachon P W, van der Sanden J J (2003). Satellite image fusion with multiscale wavelet analysis for marine applications: preserving spatial information and minimizing artifacts (PSIMA). Can J Rem Sens, 29(1): 14–23

[9] Fitzgibbon A W M P, Fisher R B (1996). Direct least squares fitting of ellipses. In: Process 13th Int’l Conf’ Pattern Recognition

[10] Gade M, Stoffelen A (2019) An introduction to microwave remote sensing of the asian seas. In: Barale V, Gade M, eds. Remote Sensing of the Asian Seas. Cham Springer

[11] Holland , G. (2008). A revised hurricane pressure–wind model. Monthly Weather Review, 9(136), 3432–3445

[12] Holland G J (1980). An analytic model of the wind and pressure profiles in hurricanes. Mon Weather Rev, 108(8): 1212–1218

[13] Holland G J, Belanger J I, Fritz A (2010). A revised model for radial profiles of hurricane winds. Mon Weather Rev, 138(12): 4393–4401

[14] Hou B, Ren B, Ju G, Li H, Jiao L, Zhao J (2016). SAR image classification via hierarchical sparse representation and multisize patch features. IEEE Geosci Remote Sens Lett, 1(13): 33–37

[15] Jin S, Li X, Yang X, Zhang J A, Shen D (2019). Identification of tropical cyclone centers in SAR imagery based on template matching and particle swarm optimization algorithms. IEEE Trans Geosci Remote Sens, 57(1): 598–608

[16] Jin S, Wang S, Li X (2014). Typhoon eye extraction with an automatic SAR image segmentation method. Inter J of Remote Sens: Remote Sens China Seas, 35 (11–12): 3978–3993

[17] Jin S, Wang S, Li X, Jiao L, Zhang J A, Shen D (2017). A salient region detection and pattern Matching-Based algorithm for center detection of a partially covered tropical cyclone in a SAR image. IEEE Trans Geosci Remote Sens, 55(1): 280–291

[18] Kanopoulos N, Vasanthavada N, Baker R L (1988). Design of an image edge detection filter using the Sobel operator. IEEE J Solid-State Circuits, 23(2): 358–367

[19] Kimball S K, Mulekar M S (2004). A 15-Year climatology of north atlantic tropical cyclones. Part I: Size parameters. J Clim, 17(18): 3555–3575

[20] Lee I K, Shamsoddini A, Li X, Trinder J C, Li Z (2016). Extracting hurricane eye morphology from spaceborne SAR images using morphological analysis. ISPRS J Photogramm, 117: 115–125

[21] Li X (2015). The first Sentinel-1 SAR image of a typhoon. Acta Oceanol Sin, 34(1): 1–2

[22] Li X, Zhang J A, Yang X, Pichel W G, DeMaria M, Long D, Li Z (2013). Tropical cyclone morphology from spaceborne synthetic aperture radar. Bull Am Meteorol Soc, 94(2): 215–230

[23] Liu K S, Chan J C L (1999). Size of tropical cyclones as inferred from ERS-1 ERS-2 data. Mon Weather Rev, 127(12): 2992–3001

[24] Lu L, Tao Y, Di L (2018). Object-based plastic-mulched landcover extraction using integrated Sentinel-1 and Sentinel-2 data. Remote Sens, 10(11): 1820

[25] Mallen K J, Montgomery M T, Wang B (2005). Reexamining the Near-Core radial structure of the tropical cyclone primary circulation: implications for vortex resiliency. J Atmos Sci, 6 2(2): 408–425

[26] Migliaccio M, Huang L, Buono A (2019). SAR speckle dependence on ocean surface wind field. IEEE Trans Geosci Remote Sens, 57(8): 5447–5455

[27] Mouche A, Chapron B, Knaff J, Zhao Y, Zhang B, Combot C (2019). Copolarized and cross-polarized SAR measurements for high-resolution description of major hurricane wind structures: application to IRMA category 5 hurricane. J Geophys Res Oceans, 124(6): 3905–3922

[28] Mouche A A, Chapron B, Zhang B, Husson R (2017). Combined Co- and Cross-Polarized SAR measurements under extreme wind conditions. IEEE Trans Geosci Remote Sens, 55(12): 6746–6755

[29] Pan H, Gao P, Zhou H, Ma R, Yang J, Zhang X (2020). Roughness analysis of sea surface from visible images by texture. IEEE Access, (8): 46448–46458

[30] Pan Y, Liu A, He S, Yang J, He M (2013). Comparison of typhoon locations over ocean surface observed by various satellite sensors. Remote Sens, 5(7): 3172–3189

[31] Shao W, Li X, Hwang P, Zhang B, Yang X (2017). Bridging the gap between cyclone wind and wave by C-band SAR measurements. J Geophys Res Oceans, 122(8): 6714–6724

[32] Shapiro L J, Willoughby H E (1982). The response of balanced hurricanes to local sources of heat and momentum. J Atmos Sci, (39): 378–394

[33] Shen W (2006). Does the size of hurricane eye matter with its intensity? Geophys Res Lett, 18(33): 18813

[34] Sitkowski M, Kossin J P, Rozoff C M (2011). Intensity and structure changes during hurricane eyewall replacement cycles. Mon Weather Rev, 139(12): 3829–3847

[35] Stoffelen A, Kumar R, Zou J, Karaev V, Chang P S, Rodriguez E (2019) Ocean Surface Vector Wind Observations. In: Barale V, Gade M, eds. Remote Sensing of the Asian Seas. Cham: Springer

[36] van Zadelhoff G J, Stoffelen A, Vachon P W, Wolfe J, Horstmann J, Belmonte Rivas M (2014). Retrieving hurricane wind speeds using cross-polarization C-band measurements. Atmos Meas Tech, 7(2): 437–449

[37] Velden C, Harper B, Wells F, Beven J L II, Zehr R, Olander T, Mayfield M, Guard C C H I P, Lander M, Edson R, Avila L, Burton A, Turk M, Kikuchi A, Christian A, Caroff P, McCroneP (2006). The Dvorak Tropical Cyclone Intensity estimation technique: a satellite-based method that has endured for over 30 years. Bull Am Meteorol Soc, 87(9): 1195–1210

[38] Vogelzang J, Stoffelen A (2017). ASCAT ultrahigh-resolution wind products on optimized grids, IEEE J Sel Topics Appl Earth Obs Rem Sensing, 10(5): 2332–2339

[39] Vogelzang J, King G P, Stoffelen A (2015). Spatial variances of wind fields and their relation to second-order structure functions and spectra. J Geophys Res Oceans, 120(2): 1048–1064

[40] Vogelzang J, Stoffelen A (2012). NWP model error structure functions obtained from scatterometer winds. IEEE Trans Geosci Remote Sens, 50(7): 2525–2533

[41] Wang H, Dong F (2009). Image features extraction of gas/liquid two-phase flow in horizontal pipeline by GLCM and GLGCM. IEEE

[42] Willoughby, H.E. (1990). Temporal changes of the primary circulation in tropical cyclones. J Atmos Sci, (47): 242–264

[43] Willoughby H E, Darling R W R, Rahn M E (2006). Parametric representation of the primary hurricane vortex. Part II: a new family of sectionally continuous profiles. Mon Weather Rev, 13 4(4): 1102–1120

[44] Wood V T, White L W, Willoughby H E, Jorgensen D P (2013). A new parametric tropical cyclone tangential wind profile model. Mon Weather Rev, 141(6): 1884–1909

[45] Ying M, Zhang W, Yu H, Lu X, Feng J, Fan Y, Zhu Y, Chen D (2014). An overview of the China meteorological administration tropical cyclone database. J Atmos Ocean Technol, 31(2): 287–301

[46] Zhang P, Chen L, Li Z, Xing J, Xing X, Yuan Z (2019). Automatic extraction of water and shadow from SAR images based on a multi-resolution dense encoder and decoder network. Sensors (Basel), 19(16): 3576

[47] Zhang B, Perrie W (2012). Cross-Polarized synthetic aperture radar: a new potential measurement technique for hurricanes. Bull Am Meteorol Soc, 93(4): 531–541

[48] Zhang G, Zhang B, Perrie W, Xu Q, He Y (2014). A hurricane tangential wind profile estimation method for C-Band Cross-Polarization SAR. IEEE Trans Geosci Remote Sens, 52(11): 7186–7194

[49] Zheng G, Yang J, Liu A K, Li X, Pichel W G, He S (2016). Comparison of typhoon centers from SAR and IR images and those from best track data sets. IEEE Trans Geosci Remote Sens, 54(2): 1000–1012

[50] Zheng G, Li X, Zhou L, Yang J, Ren L, Chen P, Zhang H, Lou X (2018). Development of a Gray-Level Co-Occurrence Matrix-Based texture orientation estimation method and its application in sea surface wind direction retrieval from SAR imagery. IEEE Trans Geosci Remote Sens, 56 (9): 5244–5260

[51] Zhou L, Lin T, Zhou X, Gao S, Wu Z, Zhang C (2020). Detection of winding faults using image features and binary tree support vector machine for autotransformer. IEEE Tran Transp Electr, 6(2): 625–634

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.

Electronic supplementary material is available in the online version of this article at http://dx.doi.org/10.1007/s11707-021-0886-9 and is accessible for authorized users.

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