Articles | Volume 16, issue 19
https://doi.org/10.5194/amt-16-4571-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-16-4571-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Oct 2023
Research article |
| 12 Oct 2023
| 12 Oct 2023
Segmentation of polarimetric radar imagery using statistical texture
Australian Bureau of Meteorology, Melbourne, Australia
Atmospheric Observations Research Group, The University of Queensland, Brisbane, Australia
Jordan P. Brook
Atmospheric Observations Research Group, The University of Queensland, Brisbane, Australia
Alain Protat
Australian Bureau of Meteorology, Melbourne, Australia
Kathryn Turner
Atmospheric Observations Research Group, The University of Queensland, Brisbane, Australia
Joshua Soderholm
Australian Bureau of Meteorology, Melbourne, Australia
Nicholas F. McCarthy
Country Fire Authority Development Team, Melbourne, Australia
Hamish McGowan
Atmospheric Observations Research Group, The University of Queensland, Brisbane, Australia
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Cited articles
Berenguer, M., Sempere-Torres, D., Corral, C., and Sánchez-Diezma, R.:
A Fuzzy Logic Technique for Identifying Nonprecipitating Echoes in Radar Scans, J. Atmos. Ocean. Tech., 23, 1157–1180, https://doi.org/10.1175/JTECH1914.1, 2006.
Brook, J. P., Protat, A., Soderholm, J. S., Warren, R. A., and McGowan, H.:
A Variational Interpolation Method for Gridding Weather Radar Data, J. Atmos. Ocean. Tech., 39 1633–1654, https://doi.org/10.1175/JTECH-D-22-0015.1, 2022.
Chandrasekar, V., Keränen, R., Lim, S., and Moisseev, D.:
Recent advances in classification of observations from dual polarization weather radars, Atmos. Res., 119, 97–111, https://doi.org/10.1016/j.atmosres.2011.08.014, 2013.
Chitalia, R. D. and Kontos, D.:
Role of texture analysis in breast MRI as a cancer biomarker: a review, J. Magn. Reson. Imaging, 49, 927–938, https://doi.org/10.1002/jmri.26556, 2019.
Clausi, D. A. and Jernigan, M. E.:
A fast method to determine co-occurrence texture features, IEEE T. Geosci. Remote, 36, 298–300, https://doi.org/10.1109/36.655338, 1998.
Davis, L. B. L.:
ProPlot: A succinct matplotlib wrapper for making beautiful, publication-quality graphics, Zenodo, https://doi.org/10.5281/zenodo.5602155, 2021.
Dempster, A. P., Laird, N. M., and Rubin, D. B.: Maximum Likelihood from Incomplete Data Via the EM Algorithm, J. Roy. Stat. Soc. B, 39, 1–22, https://doi.org/10.1111/j.2517-6161.1977.tb01600.x, 1977.
Duff, T. J., Chong, D. M., and Penman, T. D.: Quantifying wildfire growth rates using smoke plume observations derived from weather radar, Int. J. Wildland Fire, 27, 514–524, https://doi.org/10.1071/WF17180, 2018.
Gabella, M. and Notarpietro, R.: Ground clutter characterization and elimination in mountainous terrain, in: Proceedings of ERAD, 305, 311, Delft, the Netherlands, November 2002.
Giglio, L., Schroeder, W., and Justice, C. O.:
The collection 6 MODIS active fire detection algorithm and fire products, Remote Sens. Environ., 178, 31–41, https://doi.org/10.1016/j.rse.2016.02.054, 2016.
Giuli, D., Gherardelli, M., Freni, A., Seliga, T. A., and Aydin, K.:
Rainfall and Clutter Discrimination by Means of Dual-linear Polarization Radar Measurements., J. Atmos. Ocean. Tech., 8, 6, 777–789, https://doi.org/10.1175/1520-0426(1991)008<0777:RACDBM>2.0.CO;2, 1991.
Gourley, J. J., Tabary, P., and Parent du Chatelet, J.:
A Fuzzy Logic Algorithm for the Separation of Precipitating from Nonprecipitating Echoes Using Polarimetric Radar Observations. J. Atmos. Ocean. Tech., 24, 1439–1451, https://doi.org/10.1175/JTECH2035.1, 2007.
Guyot, A., Pudashine, J., Uijlenhoet, R., Protat, A., Pauwels, V. R. N., Louf, V., and Walker, J. P.:
Wildfire smoke particulate matter concentration measurements using radio links from cellular communication networks, AGU Advances, 2, e2020AV000258, https://doi.org/10.1029/2020AV000258, 2021.
Häfner, D., Nuterman, R., and Jochum, M.:
Fast, cheap, and turbulent – Global ocean modeling with GPU acceleration in Python, J. Adv. Model. Earth Sy., 13, e2021MS002717, https://doi.org/10.1029/2021MS002717, 2021.
Hall-Beyer, M.:
Practical guidelines for choosing GLCM textures to use in landscape classification tasks over a range of moderate spatial scales, Int. J. Remote Sens., 38, 1312–1338, https://doi.org/10.1080/01431161.2016.1278314, 2017.
Haralick, R. M., Shanmugam K., and Dinstein, I.:
Textural Features for Image Classification, IEEE T. Syst. Man Cybernet., 3, 610–621, https://doi.org/10.1109/TSMC.1973.4309314, 1973.
Helmus, J. J. and Collis, S. M.:
The Python ARM Radar Toolkit (Py-ART), a Library for Working with Weather Radar Data in the Python Programming Language, Journal of Open Research Software, 4, https://doi.org/10.5334/jors.119, 2016.
Hunter, J. D.:
Matplotlib: A 2D graphics environment, Comput. Sci. Eng., 9, 90–95, https://doi.org/10.1109/MCSE.2007.55, 2007.
Jatau, P., Melnikov, V., and Yu, T.:
A Machine Learning Approach for Classifying Bird and Insect Radar Echoes with S-Band Polarimetric Weather Radar, J. Atmos. Ocean. Tech., 38, 1797–1812, https://doi.org/10.1175/JTECH-D-20-0180.1, 2021.
Kane, M. T. and Mroch, A. A.:
Orthogonal Regression, the Cleary Criterion, and Lord's Paradox: Asking the Right Questions, ETS Research Report Series, Wiley online library, 2020, 1–24, https://doi.org/10.1002/ets2.12298, 2020.
Kingsmill, D. E., French, J. R., and Lareau, N. P.:
In situ microphysics observations of intense pyroconvection from a large wildfire, Atmos. Chem. Phys., 23, 1–21, https://doi.org/10.5194/acp-23-1-2023, 2023.
Lakshmanan, V., Hondl, K., Stumpf, G., and Smith, T.:
Quality control of weather radar data using texture features and a neural network, in Preprints, 31st Radar Conference, AMS 31st Radar Conference, 6–12 August 2003, Seattle WA, http://www.cimms.ou.edu/~lakshman/Papers/qcnn_pr.pdf (last access: 20 December 2022), 522–525, 2003.
Lareau, N. P., Donohoe, A., Roberts, M., and Ebrahimian, H.:
Tracking wildfires with weather radars, J. Geophys. Res.-Atmos., 127, e2021JD036158, https://doi.org/10.1029/2021JD036158, 2022.
Li, M., Zang, S., Zhang, B., Li, S., and Wu, C.:
A review of remote sensing image classification techniques: The role of spatio-contextual information, Eur. J. Remote Sens., 47, 389–411, https://doi.org/10.5721/EuJRS20144723, 2014.
Löfstedt, T., Brynolfsson, P., Asklund, T., Nyholm, T., and Garpebring, A.:
Gray-level invariant Haralick texture features, PLOS ONE, 14, e0212110, https://doi.org/10.1371/journal.pone.0212110, 2019.
Marzano, F. S., Scaranari, D., and Vulpiani, G.:
Supervised Fuzzy-Logic Classification of Hydrometeors Using C-Band Weather Radars, IEEE T. Geosci. Remote, 45, 3784–3799, https://doi.org/10.1109/TGRS.2007.903399, 2007.
McCarthy, N., McGowan, H., Guyot, A., and Dowdy, A.:
Mobile X-Pol radar: A new tool for investigating pyroconvection and associated wildfire meteorology, B. Am. Meteor Soc., 99, 1177–1195, https://doi.org/10.1175/BAMS-D-16-0118.1, 2018.
McCarthy, N., Guyot, A., Dowdy, A., and McGowan, H.:
Wildfire and weather radar: A review. J. Geophys. Res.-Atmos., 124, 266–286, https://doi.org/10.1029/2018JD029285, 2019.
McCarthy, N. F., Guyot, A., McGowan, H., and Dowdy, A.:
The use of spectrum width radar data for bushfire model verification, Paper presented at the 22nd International Congress on Modelling and Simulation, Hobart, 3–8 December 2017, Tasmania, Australia, https://www.mssanz.org.au/modsim2017/H10/mccarthy.pdf (last access: 20 December 2022), 2017.
McCarthy, N. F., Guyot, A., Protat, A., Dowdy, A. J., and McGowan, H.:
Tracking pyrometeors with meteorological radar using unsupervised machine learning, Geophys. Res. Lett., 47, https://doi.org/10.1029/2019GL084305, 2020.
NASA Earth Data: NRT VIIRS 375 m Active Fire product VJ114IMGTDL_NRT distributed from NASA FIRMS, NASA Earth Data [data set], https://doi.org/10.5067/FIRMS/VIIRS/VJ114IMGT_NRT.002, 2021a.
NASA Earth Data: MODIS Collection 61 NRT Hotspot/Active Fire Detections MCD14DL distributed from NASA FIRMS, NASA Earth Data [data set], https://doi.org/10.5067/FIRMS/MODIS/MCD14DL.NRT.0061, 2021b.
Oliveira, E. and Filho, A.: Looking at the Statistical Texture Approach Applied to Weather Radar Rainfall Fields, J. Geogr. Inf. Syst., 14, 29–39, https://doi.org/10.4236/jgis.2022.141002, 2022.
Pandas' development team:
Data structures for statistical computing in python, McKinney, Proceedings of the 9th Python in Science Conference, 28 June–3 July 2010, Austin, Texas, USA, vol. 445, 56–61, 2010.
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., and Vanderplas, J.:
Scikit-learn: Machine learning in Python, J. Mach. Learn. Res., 12, 2825–2830, 2011.
Rew, R. and Davis, G.:
NetCDF: an interface for scientific data access, IEEE Comput. Graph. Appl., 10, 76–82, https://doi.org/10.1109/38.56302, 1990.
Rossum, G.:
Python reference manual, Centre for Mathematics and Computer Science, Amsterdam, the Netherlands, 1995.
Schuur, T., Ryzhkov, A., Heinselman, P., Zrnic, D., Burgess, D., and Scharfenberg, K.:
Observations and classification of echoes with the polarimetric WSR-88D radar, Report of the National Severe Storms Laboratory, Norman, OK, 73069, p. 46, 2003.
Soderholm, J., Protat, A., and Jakob, C.:
Australian Operational Weather Radar Level 1 Dataset, National Computing Infrastructure [data set], https://doi.org/10.25914/508X-9A12, 2019.
Soderholm, J., Louf, V., Brook, J., and Protat, A.:
Australian Operational Weather Radar Level 1b Dataset, National Computing Infrastructure [data set], https://doi.org/10.25914/40KE-NM05, 2022.
Stepanian, P. M., Horton, K. G., Melnikov, V. M., Zrnić, D. S., and Gauthreaux, S. A.:
Dual-polarization radar products for biological applications, Ecosphere, 7, e01539, https://doi.org/10.1002/ecs2.1539, 2016.
Trapp, R. J. and Doswell, C. A.:
Radar Data Objective Analysis. J. Atmos. Ocean. Tech., 17, 105–120, https://doi.org/10.1175/1520-0426(2000)017<0105:RDOA>2.0.CO;2, 2000.
Van Der Walt, S., Colbert, S. C., and Varoquaux, G.:
The NumPy array: a structure for efficient numerical computation, Comput. Sci. Eng., 13, 22–30, https://doi.org/10.1109/MCSE.2011.37, 2011.
Virtanen, P., Gommers, R., Oliphant, T. E., Haberland, M., Reddy, T., Cournapeau, D., Burovski, E., Peterson, P., Weckesser, W., Bright, J., van der Walt, S. J., Brett, M., Wilson, J., Jarrod Millman, K., Mayorov, N., Nelson, A. R. J., Jones, E., Kern, R., Larson, E., Carey, C., Polat, I., Feng, Y., Moore, E. W., VanderPlas, J., Laxalde, D., Perktold, J., Cimrman, R., Henriksen, I., Quintero, E. A., Harris, C. R., Archibald, A. M., Ribeiro, A. H., Pedregosa, F., van Mulbregt, P., and SciPy 1.0 Contributors: SciPy 1.0 – Fundamental Algorithms for Scientific Computing in Python, arXiv [preprint], arXiv:1907.10121, 22 November 2019.
Vrieze, S. I.: Model selection and psychological theory: A discussion of the differences between the Akaike information criterion (AIC) and the Bayesian information criterion (BIC), Psychol. Meth., 17, 228–243, https://doi.org/10.1037/a0027127, 2012.
Wen, G., Protat, A., May, P. T., Wang, X., and Moran, W.:
A cluster-based method for hydrometeor classification using polarimetric variables. Part I: Interpretation and analysis. J. Atmos. Ocean. Tech., 32, 1320–1340, https://doi.org/10.1175/jtech-d-13-00178.1, 2015.
Woodcock, C. E. and Strahler, A. H.:
The factor of scale in remote sensing, Remote Sens. Environ., 21, 311–332, https://doi.org/10.1016/0034-4257(87)90015-0, 1987.
Yang, X., Tridandapani, S., Beitler, J. J., Yu, D. S., Yoshida, E. J., Curran, W. J., and Liu, T.:
Ultrasound GLCM texture analysis of radiation-induced parotid-gland injury in head-and-neck cancer radiotherapy: An in vivo study of late toxicity, Med. Phys., 39, 5732–5739, https://doi.org/10.1118/1.4747526, 2012.
Zrnić, D. A. S., Ryzhkov, A., Straka, J., Liu, Y., and Vivekanandan, J.:
Testing a procedure for automatic classification of hydrometeor types, J. Atmos. Ocean. Tech., 18, 892–913, https://doi.org/10.1175/1520-0426(2001)018<0892:TAPFAC>2.0.CO;2, 2001.
Zrnic, D., Zhang, P., Melnikov, V. and Mirkovic, D.:
Of fire and smoke plumes, polarimetric radar characteristics, Atmosphere, 11, 363, https://doi.org/10.3390/atmos11040363, 2020.
Short summary
We propose a new method that should facilitate the use of weather radars to study wildfires. It is important to be able to identify the particles emitted by wildfires on radar, but it is difficult because there are many other echoes on radar like clear air, the ground, sea clutter, and precipitation. We came up with a two-step process to classify these echoes. Our method is accurate and can be used by fire departments in emergencies or by scientists for research.
We propose a new method that should facilitate the use of weather radars to study wildfires. It...
