{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,11]],"date-time":"2026-07-11T14:05:17Z","timestamp":1783778717424,"version":"3.55.0"},"reference-count":30,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,13]],"date-time":"2023-01-13T00:00:00Z","timestamp":1673568000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Yunnan and Key Projects of Yunnan Basic Research Plan","award":["202101AS070016"],"award-info":[{"award-number":["202101AS070016"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>As an important part of hydrometry, river discharge monitoring plays an irreplaceable role in the planning and management of water resources and is an essential element and necessary means of river management. Due to its benefits of simplicity, efficiency and safety, Space-Time Image Velocimetry (STIV) has attracted attention from all around the world. The most crucial component of the STIV is the detection of the Main Orientation of Texture (MOT), and the precision of detection directly affects the results of calculations. However, due to the complicated river flow characteristics and the harsh testing environment in the field, a large amount of noise and interfering textures show up in the space-time images, which affects the detection results of the MOT. In response to the shortage of noise and interference texture, a new non-contact image analysis method is developed. Firstly, Multi-scale Retinex (MSR) is proposed to pre-process the images for contrast enhancement; secondly, a fourth-order Gaussian derivative steerable filter is employed to enhance the structure of the texture; next, based on the probability density distribution function and the orientations of the enhanced images, the noise suppression function and the orientation-filtering function are designed to filter out the noise to highlight the texture. Finally, the Fourier Maximum Angle Analysis (FMAA) is used to filter out the noise further and obtain the clear orientations to achieve the measurement of velocity and discharge. The experimental results show that, compared with the widely used image velocimetry measurements, the accuracy of our method in the average velocity and flow discharge is significantly improved, and the real-time performance is excellent.<\/jats:p>","DOI":"10.3390\/s23020955","type":"journal-article","created":{"date-parts":[[2023,1,16]],"date-time":"2023-01-16T05:30:07Z","timestamp":1673847007000},"page":"955","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":19,"title":["Velocity Vector Estimation of Two-Dimensional Flow Field Based on STIV"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6195-3225","authenticated-orcid":false,"given":"Jianghuai","family":"Lu","sequence":"first","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaohong","family":"Yang","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9659-0452","authenticated-orcid":false,"given":"Jianping","family":"Wang","sequence":"additional","affiliation":[{"name":"Faculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Watanabe, K., Fujita, I., Iguchi, M., and Hasegawa, M. (2021). Improving accuracy and robustness of space-time image velocimetry (STIV) with deep learning. Water, 13.","DOI":"10.3390\/w13152079"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"033015","DOI":"10.1088\/1757-899X\/688\/3\/033015","article-title":"Design of Acoustic Doppler Current Profiler Calibration System","volume":"688","author":"Xie","year":"2019","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"101846","DOI":"10.1016\/j.flowmeasinst.2020.101846","article-title":"A methodology for improving both performance and measurement errors in PIV","volume":"77","author":"Maceas","year":"2021","journal-title":"Flow Meas. Instrum."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"104866","DOI":"10.1016\/j.cageo.2021.104866","article-title":"Development of a three-axis accelerometer and large-scale particle image velocimetry (LSPIV) to enhance surface velocity measurements in rivers","volume":"155","author":"Liu","year":"2021","journal-title":"Comput. Geosci."},{"key":"ref_5","first-page":"1","article-title":"New system for rapid measurement of indoor surface velocity fields based on feature matching velocimetry","volume":"38","author":"Cao","year":"2019","journal-title":"J. Hydroelectr. Eng."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1016\/j.flowmeasinst.2018.11.009","article-title":"Optical flow for image-based river velocity estimation","volume":"65","author":"Khalid","year":"2019","journal-title":"Flow Meas. Instrum."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"537","DOI":"10.3741\/JKWRA.2014.47.6.537","article-title":"Measurement of two-dimensional velocity distribution of spatio-temporal image velocimeter using cross-correlation analysis","volume":"47","author":"Yu","year":"2014","journal-title":"J. Korea Water Resour. Assoc."},{"key":"ref_8","first-page":"I_829","article-title":"Development of aerial STIV applied to videotaped movie from multicopter based on high-accurate image stabilization method","volume":"71","author":"Fujita","year":"2015","journal-title":"J. Jpn. Soc. Civ. Eng. Ser. B1"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"469","DOI":"10.3741\/JKWRA.2016.49.6.469","article-title":"Development of a real-time surface image velocimeter using an android smartphone","volume":"49","author":"Yu","year":"2016","journal-title":"J. Korea Water Resour. Assoc."},{"key":"ref_10","first-page":"933","article-title":"Calculation of surface image velocity fields by analyzing spatio-temporal volumes with the fast Fourier transform","volume":"54","author":"Yu","year":"2021","journal-title":"J. Korea Water Resour. Assoc."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"141","DOI":"10.20481\/kscdp.2021.8.3.141","article-title":"Evaluation of the Applicability of STIV to Wave Characteristic Measurement in the Swash Zone","volume":"8","author":"Kim","year":"2021","journal-title":"Korea Soc. Coast. Disaster Prev."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"397","DOI":"10.1080\/00221689809498626","article-title":"Large-scale particle image velocimetry for flow analysis in hydraulic engineering applications","volume":"36","author":"Fujita","year":"1998","journal-title":"J. Hydraul. Res."},{"key":"ref_13","unstructured":"Zuniga Zamalloa, C.C., and Landry, B.J. (2017, January 9\u201312). Large scale feature tracking velocimetry for retrieving velocity of surface waters. Proceedings of the Hydraulic Measurements & Experimental Methods Conference, Durham, NH, USA."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Cao, L., Weitbrecht, V., Li, D., and Detert, M. (2018, January 5\u20138). Feature tracking velocimetry applied to airborne measurement data from Murg Creek. Proceedings of the Ninth International Conference on Fluvial Hydraulics (River Flow 2018), Lyon-Villeurbanne, France.","DOI":"10.1051\/e3sconf\/20184005030"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Tauro, F., Tosi, F., Mattoccia, S., Toth, E., Piscopia, R., and Grimaldi, S. (2018). Optical tracking velocimetry (OTV): Leveraging optical flow and trajectory-based filtering for surface streamflow observations. Remote Sens., 10.","DOI":"10.3390\/rs10122010"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s00348-019-2787-1","article-title":"A field-segmentation-based variational optical flow method for PIV measurements of nonuniform flows","volume":"60","author":"Lu","year":"2019","journal-title":"Exp. Fluids"},{"key":"ref_17","first-page":"25","article-title":"A Method of Measuring One-Dimensional River Surface Flow Using Spatiotemporal Images","volume":"21","author":"Fujiia","year":"2001","journal-title":"J. Vis. Soc. Jpn."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1","DOI":"10.12677\/JWRR.2020.91001","article-title":"Application of Flow Measurement Method Based on Space-Time Image Recognition of River Surface","volume":"9","author":"Zhao","year":"2020","journal-title":"J. Water Resour. Res."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1363","DOI":"10.1007\/s10652-018-9651-3","article-title":"Efficient and accurate estimation of water surface velocity in STIV","volume":"19","author":"Fujita","year":"2019","journal-title":"Environm. Fluid Mech."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Zhang, Z., Li, H., Zhou, Y., and Huang, J. (2019, January 1\u20133). Design and evaluation of an FFT-based space-time image velocimetry (STIV) for time-averaged velocity measurement. Proceedings of the 2019 14th IEEE International Conference on Electronic Measurement & Instruments (ICEMI), Changsha, China.","DOI":"10.1109\/ICEMI46757.2019.9101763"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"111218","DOI":"10.1016\/j.measurement.2022.111218","article-title":"Measurement of Debris Flow Velocity in Flume Using Normal Image by Space-Time Image Velocimetry Incorporated with Machine Learning","volume":"199","author":"Kim","year":"2022","journal-title":"Measurement"},{"key":"ref_22","unstructured":"Li, H., Zhang, Z., Meng, J., Sun, Y., and Cui, W. (2022). River surface space-time image velocimetry of based on residual network. J. Hohai Univ. (Nat. Sci.), 1\u201316. Available online: http:\/\/kns.cnki.net\/kcms\/detail\/32.1117.TV.20220602.1457.002.html."},{"key":"ref_23","first-page":"397","article-title":"Multiscale Retinex infrared image enhancement based on the fusion of guided filtering and logarithmic transformation algorithm","volume":"44","author":"Chen","year":"2022","journal-title":"Infrared Technol."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"1007","DOI":"10.1109\/TPAMI.2004.44","article-title":"Design of steerable filters for feature detection using canny-like criteria","volume":"26","author":"Jacob","year":"2004","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"045803","DOI":"10.1088\/0957-0233\/22\/4\/045803","article-title":"Automatic tracking and measurement of the motion of blood cells in microvessels based on analysis of multiple spatiotemporal images","volume":"22","author":"Chen","year":"2011","journal-title":"Meas. Sci. Technol."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"094015","DOI":"10.1088\/1361-6501\/ab808a","article-title":"Application of masked two-dimensional Fourier spectra for improving the accuracy of STIV-based river surface flow velocity measurements","volume":"31","author":"Fujita","year":"2020","journal-title":"Meas. Sci. Technol."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"101864","DOI":"10.1016\/j.flowmeasinst.2020.101864","article-title":"An improvement of the Space-Time Image Velocimetry combined with a new denoising method for estimating river discharge","volume":"77","author":"Zhao","year":"2021","journal-title":"Flow Meas. Instrum."},{"key":"ref_28","first-page":"43","article-title":"Sensitivity analysis of image filter for space-time image velocimetry in frequency domain","volume":"43","author":"Zhang","year":"2022","journal-title":"Chin. J. Sci. Instrum."},{"key":"ref_29","unstructured":"(2015). Code for Liquid Flow Measurement in Open Channels (Standard No. GB 50179-2015)."},{"key":"ref_30","first-page":"195","article-title":"Image Flow Measurement Based on the Combination of Frame Difference and Fast and Dense Optical Flow","volume":"54","author":"Wang","year":"2022","journal-title":"Adv. Eng. Sci."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/955\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:05:47Z","timestamp":1760119547000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/2\/955"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,13]]},"references-count":30,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23020955"],"URL":"https:\/\/doi.org\/10.3390\/s23020955","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,13]]}}}