{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,12]],"date-time":"2025-11-12T14:27:59Z","timestamp":1762957679926,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T00:00:00Z","timestamp":1755561600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Guangdong Provincial Natural Science Foundation","award":["2024A1515030159","52101358","41906154","2024ZDZX3038","SL2024A04J01461"],"award-info":[{"award-number":["2024A1515030159","52101358","41906154","2024ZDZX3038","SL2024A04J01461"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["2024A1515030159","52101358","41906154","2024ZDZX3038","SL2024A04J01461"],"award-info":[{"award-number":["2024A1515030159","52101358","41906154","2024ZDZX3038","SL2024A04J01461"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Special Projects in Key Areas of Regular Higher Education Institutions in Guangdong Province","award":["2024A1515030159","52101358","41906154","2024ZDZX3038","SL2024A04J01461"],"award-info":[{"award-number":["2024A1515030159","52101358","41906154","2024ZDZX3038","SL2024A04J01461"]}]},{"name":"Special Topics on Basic and Applied Basic Research in Guangzhou","award":["2024A1515030159","52101358","41906154","2024ZDZX3038","SL2024A04J01461"],"award-info":[{"award-number":["2024A1515030159","52101358","41906154","2024ZDZX3038","SL2024A04J01461"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Sea surface wind speed is a key parameter in marine meteorology, navigation safety, and offshore engineering. Traditional marine radar wind speed retrieval algorithms often suffer from poor environmental adaptability and limited applicability across different radar systems, while existing empirical models face challenges in accuracy and generalization. To address these issues, this study proposes a novel wind speed retrieval method based on X-band marine radar image sequences and texture features derived from the Gray-Level Co-occurrence Matrix (GLCM). A three-stage preprocessing pipeline\u2014comprising noise suppression, geometric correction, and interpolation\u2014is employed to extract small-scale wind streaks that reflect wind field characteristics, ensuring high-quality image data. Two key GLCM texture features of wind streaks, energy and entropy, are identified, and their stable values are used to construct a segmented dual-parameter wind speed model with a division at 10 m\/s. Experimental results show that both energy- and entropy-based models outperform traditional empirical models, reducing mean errors by approximately 49.3% and 16.7%, respectively. The energy stable model achieves the best overall performance with a correlation coefficient of 0.89, while the entropy stable model demonstrates superior performance at low wind speeds. The complementary nature of the two models enhances robustness under varying conditions, providing a more accurate and efficient solution for sea surface wind speed retrieval.<\/jats:p>","DOI":"10.3390\/e27080877","type":"journal-article","created":{"date-parts":[[2025,8,19]],"date-time":"2025-08-19T15:29:29Z","timestamp":1755617369000},"page":"877","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Sea Surface Wind Speed Retrieval from Marine Radar Image Sequences Based on GLCM-Derived Texture Features"],"prefix":"10.3390","volume":"27","author":[{"given":"Hui","family":"Wang","sequence":"first","affiliation":[{"name":"School of Low-Altitude Equipment and Intelligent Control, Guangzhou Maritime University, Guangzhou 510725, China"}]},{"given":"Haiyang","family":"Qiu","sequence":"additional","affiliation":[{"name":"School of Low-Altitude Equipment and Intelligent Control, Guangzhou Maritime University, Guangzhou 510725, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1887-6222","authenticated-orcid":false,"given":"Lei","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, China"}]},{"given":"Jingxi","family":"Huang","sequence":"additional","affiliation":[{"name":"School of Low-Altitude Equipment and Intelligent Control, Guangzhou Maritime University, Guangzhou 510725, China"}]},{"given":"Xingbo","family":"Ruan","sequence":"additional","affiliation":[{"name":"School of Low-Altitude Equipment and Intelligent Control, Guangzhou Maritime University, Guangzhou 510725, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Jafari, Z., Bobby, P., Karami, E., and Taylor, R. (2024). A Novel Method for the Estimation of Sea Surface Wind Speed from SAR Imagery. J. Mar. Sci. Eng., 12.","DOI":"10.3390\/jmse12101881"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Huang, W., Liu, X., and Gill, E.W. (2017). Ocean wind and wave measurements using X-band marine radar: A comprehensive review. Remote Sens., 9.","DOI":"10.3390\/rs9121261"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Wang, H., Qiu, H., Zhi, P., Wang, L., Chen, W., Akhtar, R., and Raja, M.A.Z. (2019). Study of Algorithms for Wind Direction Retrieval from X-Band Marine Radar Images. Electronics, 8.","DOI":"10.3390\/electronics8070764"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"10938","DOI":"10.1109\/JSTARS.2024.3405736","article-title":"Optimized Estimation of Azimuth Cutoff for Retrieval of Significant Wave Height and Wind Speed From Polarimetric Gaofen-3 SAR Wave Mode Data","volume":"17","author":"Zheng","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"10367","DOI":"10.1109\/JSTARS.2024.3407115","article-title":"On the Use of Azimuth Cutoff for Sea Surface Wind Speed Retrieval From SAR","volume":"17","author":"Zhu","year":"2024","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Horstmann, J., B\u00f6dewadt, J., Cysewski, M., Seemann, J., and Stre\u03b2er, M. (2021). A coherent on receive X-band marine radar for ocean observations. Sensors, 21.","DOI":"10.3390\/s21237828"},{"key":"ref_7","first-page":"4201711","article-title":"Spatial\u2013temporal convolutional gated recurrent unit network for significant wave height estimation from shipborne marine radar data. IEEE Trans","volume":"60","author":"Chen","year":"2021","journal-title":"Geosci. Remote Sens."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"204880","DOI":"10.1109\/ACCESS.2020.3037157","article-title":"A Method for retrieving wave parameters from synthetic X-band marine radar images","volume":"8","author":"Wei","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"2988","DOI":"10.1109\/TGRS.2015.2509781","article-title":"Surface current measurements using X-band marine radar with vertical polarization","volume":"54","author":"Huang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"2115","DOI":"10.1109\/TGRS.2019.2953143","article-title":"Rain detection from X-band marine radar images: A support vector machine-based approach","volume":"58","author":"Chen","year":"2020","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1109\/JSTARS.2020.3043739","article-title":"Rain-contaminated region segmentation of X-band marine radar images with an ensemble of SegNets","volume":"14","author":"Chen","year":"2020","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Lu, Z., Tian, C., Wei, Y., and Liu, F. (2023). A Method for Estimating Ship Surface Wind Parameters by Combining Anemometer and X-Band Marine Radar Data. Remote Sens., 15.","DOI":"10.3390\/rs15225392"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"112793","DOI":"10.1016\/j.oceaneng.2022.112793","article-title":"Multi-anemometer optimal layout and weighted fusion method for estimation of ship surface steady-state wind parameters","volume":"266","author":"Zhang","year":"2022","journal-title":"Ocean Eng."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"534","DOI":"10.1109\/JOE.2005.857524","article-title":"Wind-and wave-field measurements using marine X-band radar-image sequences","volume":"30","author":"Dankert","year":"2005","journal-title":"IEEE J. Ocean. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Wang, H., Fan, D., Qiu, H., Zhu, Z., Zhi, P., and Zhu, W. (2021, January 10\u201313). An Improved RBF Neural Network based on Clustering Algorithm for Estimating Wind Speed from X-band Marine Radar Images. Proceedings of the 2021 4th IEEE International Conference on Industrial Cyber-Physical Systems (ICPS), Victoria, BC, Canada.","DOI":"10.1109\/ICPS49255.2021.9468214"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1312","DOI":"10.1109\/LGRS.2018.2845698","article-title":"Wind speed estimation from X-band marine radar images using support vector regression method","volume":"15","author":"Chen","year":"2018","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Yang, Z., and Huang, W. (2024, January 14\u201318). A CNN-based Hybrid Dehazing and Regression Model for Sea Surface Wind Speed Retrieval from Rain-contaminated Marine Radar Data. Proceedings of the OCEANS 2024-Singapore, Singapore.","DOI":"10.1109\/OCEANS51537.2024.10682253"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"5105813","DOI":"10.1109\/TGRS.2025.3583575","article-title":"WSTCNN: A Wavelet Scattering Transform-CNN Model for Wind Speed Estimation From Radar Images","volume":"63","author":"Yang","year":"2025","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_19","unstructured":"Hatten, H., Ziemer, F., Seemann, J., and Nieto-Borge, J. (1998, January 5\u20139). Correlation between the Spectral Background Noise of a Nautical Radar and the Wind Vector. Proceedings of the 17th International Conference on Offshore Mechanics and Arctic Engineering (OMAE), Lisbon, Portugal."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"1404","DOI":"10.1016\/j.oceaneng.2004.11.005","article-title":"Analysis of sea waves and wind from X-band radar","volume":"32","author":"Izquierdo","year":"2005","journal-title":"Ocean Eng."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"3800","DOI":"10.1109\/TGRS.2012.2186457","article-title":"Wind retrieval from shipborne nautical X-band radar data","volume":"50","author":"Lund","year":"2012","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1175\/JTECH-D-12-00027.1","article-title":"Real-time ocean wind vector retrieval from marine radar image sequences acquired at grazing angle","volume":"30","author":"Horstmann","year":"2013","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"896","DOI":"10.1109\/JSTARS.2014.2357426","article-title":"Comparison of algorithms for wind parameters extraction from shipborne X-band marine radar images","volume":"8","author":"Liu","year":"2014","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"79","DOI":"10.1016\/j.oceaneng.2014.12.019","article-title":"Determination of nearshore sea surface wind vector from marine X-band radar images","volume":"96","author":"Chen","year":"2015","journal-title":"Ocean Eng."},{"key":"ref_25","first-page":"799","article-title":"Sea wind direction extraction algorithm by X-band radar in moving platform","volume":"38","author":"Lu","year":"2016","journal-title":"Syst. Eng. Electron"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1833","DOI":"10.1109\/TGRS.2016.2635078","article-title":"Wind direction estimation from rain-contaminated marine radar data using the ensemble empirical mode decomposition method","volume":"55","author":"Liu","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"28","DOI":"10.1109\/MMUL.2010.88","article-title":"Using texture analysis for medical diagnosis","volume":"19","author":"Parekh","year":"2012","journal-title":"IEEE Multimed."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1049\/iet-ipr.2014.0202","article-title":"Focal and diffused liver disease classification from ultrasound images based on isocontour segmentation","volume":"9","author":"Krishnan","year":"2015","journal-title":"IET Image Process."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"5244","DOI":"10.1109\/TGRS.2018.2812778","article-title":"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","volume":"56","author":"Zheng","year":"2018","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_30","first-page":"1","article-title":"Sea surface wind speed retrieval from textures in synthetic aperture radar imagery","volume":"60","author":"Zhou","year":"2021","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Horstmann, J., and Dankert, H. (August, January 31). Estimation of friction velocity using tower based marine radars. Proceedings of the 2006 IEEE International Symposium on Geoscience and Remote Sensing, Denver, CO, USA.","DOI":"10.1109\/IGARSS.2006.342"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"4074","DOI":"10.1109\/JSTARS.2021.3069989","article-title":"An energy spectrum algorithm for wind direction retrieval from X-band marine radar image sequences","volume":"14","author":"Wang","year":"2021","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"252","DOI":"10.1109\/LGRS.2015.2508284","article-title":"An algorithm for wind direction retrieval from X-band marine radar images","volume":"13","author":"Wang","year":"2016","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_34","unstructured":"Dankert, H., Horstmann, J., Magnusson, A.K., and Rosenthal, W. (2003, January 21\u201325). Ocean Winds Retrieved from X-band Radar-Image Sequences. Proceedings of the International Geoscience and Remote Sensing Symposium (IGARSS), Toulouse, France."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Wang, H., Li, S., Qiu, H., Lu, Z., Wei, Y., Zhu, Z., and Ge, H. (2023). Development of a fast convergence gray-level co-occurrence matrix for sea surface wind direction extraction from marine radar images. Remote Sens., 15.","DOI":"10.3390\/rs15082078"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1629","DOI":"10.1175\/JTECH2083.1","article-title":"A marine radar wind sensor","volume":"24","author":"Heiko","year":"2007","journal-title":"J. Atmos. Ocean. Technol."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Liu, Z., Barlow, J.F., Chan, P.W., Fung, J.C.H., Li, Y., Ren, C., Mak, H.W.L., and Ng, E. (2019). A review of progress and applications of pulsed Doppler wind LiDARs. Remote Sens., 11.","DOI":"10.3390\/rs11212522"},{"key":"ref_38","unstructured":"Hatten, H., Seemann, J., Horstmann, J., and Ziemer, F. (1998, January 6\u201310). Azimuthal dependence of the radar cross section and the spectral background noise of a nautical radar at grazing incidence. Proceedings of the International Geoscience and Remote Sensing Symposium, Seattle, WA, USA."},{"key":"ref_39","first-page":"591","article-title":"Image texture classification using gray level co-occurrence matrix based statistical features","volume":"75","author":"Suresh","year":"2012","journal-title":"Eur. J. Sci. Res."}],"container-title":["Entropy"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/8\/877\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T18:31:08Z","timestamp":1760034668000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1099-4300\/27\/8\/877"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,8,19]]},"references-count":39,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["e27080877"],"URL":"https:\/\/doi.org\/10.3390\/e27080877","relation":{},"ISSN":["1099-4300"],"issn-type":[{"type":"electronic","value":"1099-4300"}],"subject":[],"published":{"date-parts":[[2025,8,19]]}}}