{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:45:57Z","timestamp":1760402757750,"version":"build-2065373602"},"reference-count":44,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T00:00:00Z","timestamp":1617148800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100007694","name":"Korea Agency for Infrastructure Technology Advancement","doi-asserted-by":"publisher","award":["21DPIW-C153746-03"],"award-info":[{"award-number":["21DPIW-C153746-03"]}],"id":[{"id":"10.13039\/501100007694","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fluvial remote sensing has been used to monitor diverse riverine properties through processes such as river bathymetry and visual detection of suspended sediment, algal blooms, and bed materials more efficiently than laborious and expensive in-situ measurements. Red\u2013green\u2013blue (RGB) optical sensors have been widely used in traditional fluvial remote sensing. However, owing to their three confined bands, they rely on visual inspection for qualitative assessments and are limited to performing quantitative and accurate monitoring. Recent advances in hyperspectral imaging in the fluvial domain have enabled hyperspectral images to be geared with more than 150 spectral bands. Thus, various riverine properties can be quantitatively characterized using sensors in low-altitude unmanned aerial vehicles (UAVs) with a high spatial resolution. Many efforts are ongoing to take full advantage of hyperspectral band information in fluvial research. Although geo-referenced hyperspectral images can be acquired for satellites and manned airplanes, few attempts have been made using UAVs. This is mainly because the synthesis of line-scanned images on top of image registration using UAVs is more difficult owing to the highly sensitive and heavy image driven by dense spatial resolution. Therefore, in this study, we propose a practical technique for achieving high spatial accuracy in UAV-based fluvial hyperspectral imaging through efficient image registration using an optical flow algorithm. Template matching algorithms are the most common image registration technique in RGB-based remote sensing; however, they require many calculations and can be error-prone depending on the user, as decisions regarding various parameters are required. Furthermore, the spatial accuracy of this technique needs to be verified, as it has not been widely applied to hyperspectral imagery. The proposed technique resulted in an average reduction of spatial errors by 91.9%, compared to the case where the image registration technique was not applied, and by 78.7% compared to template matching.<\/jats:p>","DOI":"10.3390\/s21072407","type":"journal-article","created":{"date-parts":[[2021,3,31]],"date-time":"2021-03-31T10:24:33Z","timestamp":1617186273000},"page":"2407","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Development of an Image Registration Technique for Fluvial Hyperspectral Imagery Using an Optical Flow Algorithm"],"prefix":"10.3390","volume":"21","author":[{"given":"Hojun","family":"You","sequence":"first","affiliation":[{"name":"IIHR\u2014Hydroscience and Engineering, University of Iowa, Iowa City, IA 52242, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4536-767X","authenticated-orcid":false,"given":"Dongsu","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Civil and Environmental Engineering, Dankook University, Yongin-si 16890, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"46","DOI":"10.1016\/j.ecolmodel.2008.11.017","article-title":"Development of a framework for fire risk assessment using remote sensing and geographic information system technologies","volume":"221","author":"Chuvieco","year":"2010","journal-title":"Ecol. Model."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"10078","DOI":"10.3390\/rs70810078","article-title":"Application of multispectral sensors carried on unmanned aerial vehicle (UAV) to trophic state mapping of small reservoirs: A case study of Tain-Pu reservoir in Kinmen, Taiwan","volume":"7","author":"Su","year":"2015","journal-title":"Remote Sens."},{"key":"ref_3","first-page":"85","article-title":"Unmanned Aerial Vehicles in Environmental Monitoring\u2014An Efficient Way for Remote Sensing","volume":"4","author":"Juzsakova","year":"2015","journal-title":"J. Environ. Sci. Eng. A"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"74","DOI":"10.1016\/j.geomorph.2010.09.030","article-title":"Making riverscapes real","volume":"137","author":"Carbonneau","year":"2012","journal-title":"Geomorphology"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1006\/jema.2001.0509","article-title":"Land use change analysis in the Zhujiang Delta of China using satellite remote sensing, GIS and stochastic modelling","volume":"64","author":"Weng","year":"2002","journal-title":"J. Environ. Manag."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.rse.2004.11.015","article-title":"Oil spill detection by satellite remote sensing","volume":"95","author":"Brekke","year":"2005","journal-title":"Remote Sens. Environ."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1427","DOI":"10.1002\/(SICI)1099-1085(199708)11:10<1427::AID-HYP473>3.0.CO;2-S","article-title":"Satellite remote sensing of river inundation area, stage, and discharge: A review","volume":"11","author":"Smith","year":"1997","journal-title":"Hydrol. Process."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"483","DOI":"10.1641\/0006-3568(2002)052[0483:LTRBTG]2.0.CO;2","article-title":"Landscapes to riverscapes: Bridging the gap between research and conservation of stream fishes","volume":"52","author":"Fausch","year":"2002","journal-title":"BioScience"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"799","DOI":"10.1046\/j.1365-2427.2002.00909.x","article-title":"Remote sensing of riverine landscapes","volume":"47","author":"Mertes","year":"2002","journal-title":"Freshw. Biol."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1867","DOI":"10.1002\/esp.2094","article-title":"Remote sensing of rivers: The emergence of a subdiscipline in the river sciences","volume":"35","author":"Marcus","year":"2010","journal-title":"Earth Surf. Process. Landf."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"34","DOI":"10.2112\/JCOASTRES-D-11-00051.1","article-title":"Remote sensing of algal blooms: An overview with case studies","volume":"28","author":"Klemas","year":"2012","journal-title":"J. Coast. Res."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1002\/rra.2743","article-title":"Hyperspatial Remote Sensing of Channel Reach Morphology and Hydraulic Fish Habitat Using an Unmanned Aerial Vehicle (UAV): A First Assessment in the Context of River Research and Management","volume":"31","author":"Tamminga","year":"2015","journal-title":"River Res. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Marcus, A.W. (2012). Remote Sensing of the Hydraulic Environment in Gravel-Bed Rivers. Gravel-Bed Rivers: Processes, Tools, Environments, John Wiley and Sons.","DOI":"10.1002\/9781119952497.ch21"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Carbonneau, P.E., and Pi\u00e9gay, H. (2012). Fluvial Remote Sensing for Science and Management, John Wiley & Sons, Ltd.","DOI":"10.1002\/9781119940791"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"588","DOI":"10.1007\/s12205-017-1862-5","article-title":"Applications of unmanned aerial vehicles in fluvial remote sensing: An overview of recent achievements","volume":"22","author":"Rhee","year":"2018","journal-title":"KSCE J. Civ. Eng."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"254","DOI":"10.1016\/j.rse.2012.11.023","article-title":"Airborne hyperspectral remote sensing to assess spatial distribution of water quality characteristics in large rivers: The Mississippi River and its tributaries in Minnesota","volume":"130","author":"Olmanson","year":"2013","journal-title":"Remote Sens. Environ."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"2971","DOI":"10.3390\/rs70302971","article-title":"Intercomparison of UAV, aircraft and satellite remote sensing platforms for precision viticulture","volume":"7","author":"Matese","year":"2015","journal-title":"Remote Sens."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Seifert, E., Seifert, S., Vogt, H., Drew, D., van Aardt, J., Kunneke, A., and Seifert, T. (2019). Influence of drone altitude, image overlap, and optical sensor resolution on multi-view reconstruction of forest images. Remote Sens., 11.","DOI":"10.3390\/rs11101252"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"935","DOI":"10.1016\/j.ijleo.2016.11.057","article-title":"Implementation and analysis of template matching for image registration on DevKit-8500D","volume":"130","author":"Thakar","year":"2017","journal-title":"Optik."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"9059","DOI":"10.1109\/TGRS.2019.2924684","article-title":"Fast and Robust Matching for Multimodal Remote Sensing Image Registration","volume":"57","author":"Ye","year":"2019","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"38544","DOI":"10.1109\/ACCESS.2018.2853100","article-title":"Multi-Temporal Remote Sensing Image Registration Using Deep Convolutional Features","volume":"6","author":"Yang","year":"2018","journal-title":"IEEE Access"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1016\/j.isprsjprs.2017.12.012","article-title":"A deep learning framework for remote sensing image registration","volume":"145","author":"Wang","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"2576","DOI":"10.1080\/01431161.2018.1528402","article-title":"High-resolution image registration based on improved SURF detector and localized GTM","volume":"40","author":"Sedaghat","year":"2019","journal-title":"Int. J. Remote Sens."},{"key":"ref_24","first-page":"103","article-title":"Remote sensing image registration techniques: A survey","volume":"Volume 6134 LNCS","author":"Dawn","year":"2010","journal-title":"Proceedings of the Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.isprsjprs.2019.03.002","article-title":"Robust registration for remote sensing images by combining and localizing feature- and area-based methods","volume":"151","author":"Feng","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_26","unstructured":"Harris, C., and Stephens, M. (September, January 31). A Combined Corner and Edge Detector. Proceedings of the Alvey Vision Conference, Manchester, UK."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"305","DOI":"10.5201\/ipol.2018.229","article-title":"An analysis and implementation of the harris corner detector","volume":"8","author":"Salgado","year":"2018","journal-title":"Image Process. Line"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"43","DOI":"10.1007\/BF01420984","article-title":"Performance of Optical Flow Techniques","volume":"12","author":"Barron","year":"1994","journal-title":"Int. J. Comput. Vis."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1016\/j.jsb.2003.09.033","article-title":"Automatic particle selection: Results of a comparative study","volume":"145","author":"Glaeser","year":"2004","journal-title":"J. Struct. Biol."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Douini, Y., Riffi, J., Mahraz, M.A., and Tairi, H. (2017). Solving sub-pixel image registration problems using phase correlation and Lucas-Kanade optical flow method. Intell. Syst. Comput. Vis. ISCV.","DOI":"10.1109\/ISACV.2017.8054948"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3043","DOI":"10.1109\/JSTARS.2019.2917703","article-title":"Object Tracking in Satellite Videos Based on a Multiframe Optical Flow Tracker","volume":"12","author":"Du","year":"2019","journal-title":"IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"977","DOI":"10.1016\/S0262-8856(03)00137-9","article-title":"Image registration methods: A survey","volume":"21","author":"Flusser","year":"2003","journal-title":"Image Vis. Comput."},{"key":"ref_33","first-page":"120","article-title":"Fast Template Matching Template","volume":"10","author":"Lewis","year":"1995","journal-title":"Pattern Recognit."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1117\/12.421129","article-title":"Template matching using fast normalized cross correlation","volume":"4387","author":"Briechle","year":"2001","journal-title":"Opt. Pattern Recognit. XII"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Jiang, H., and Yu, S.X. (2009, January 20\u201325). Linear solution to scale and rotation invariant object matching. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, CVPR Workshops 2009, Miami Beach, FL, USA.","DOI":"10.1109\/CVPR.2009.5206776"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Yan, B., Xiao, L., Zhang, H., Xu, D., Ruan, L., Wang, Z., and Zhang, Y. (2019). An adaptive template matching-based single object tracking algorithm with parallel acceleration. J. Vis. Commun. Image Represent., 64.","DOI":"10.1016\/j.jvcir.2019.102603"},{"key":"ref_37","unstructured":"Moravec, H. (1980). Obstacle Avoidance and Navigation in the Real World by a Seeing Robot Rover. Tech. Report CMU-RI-TR-80-03, Stanford Univ CA Dept of Computer Science."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"319","DOI":"10.1117\/12.965761","article-title":"Determining Optical Flow","volume":"Volume 0281","author":"Pearson","year":"1981","journal-title":"Proceedings of the Techniques and Applications of Image Understanding"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"433","DOI":"10.1145\/212094.212141","article-title":"The Computation of Optical Flow","volume":"27","author":"Beauchemin","year":"1995","journal-title":"ACM Comput. Surv. (CSUR)"},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"97","DOI":"10.1177\/0309133313515293","article-title":"Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography","volume":"38","author":"Lucieer","year":"2014","journal-title":"Prog. Phys. Geogr."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1392","DOI":"10.3390\/rs4051392","article-title":"An automated technique for generating georectified mosaics from ultra-high resolution Unmanned Aerial Vehicle (UAV) imagery, based on Structure from Motion (SFM) point clouds","volume":"4","author":"Turner","year":"2012","journal-title":"Remote Sens."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"300","DOI":"10.1016\/j.geomorph.2012.08.021","article-title":"\u201cStructure-from-Motion\u201d photogrammetry: A low-cost, effective tool for geoscience applications","volume":"179","author":"Westoby","year":"2012","journal-title":"Geomorphology"},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Sanz-Ablanedo, E., Chandler, J.H., Rodr\u00edguez-P\u00e9rez, J.R., and Ord\u00f3\u00f1ez, C. (2018). Accuracy of Unmanned Aerial Vehicle (UAV) and SfM photogrammetry survey as a function of the number and location of ground control points used. Remote Sens., 10.","DOI":"10.3390\/rs10101606"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1425","DOI":"10.1080\/01431169608948714","article-title":"The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features","volume":"17","author":"McFeeters","year":"1996","journal-title":"Int. J. Remote Sens."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/7\/2407\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:55:48Z","timestamp":1760363748000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/21\/7\/2407"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,31]]},"references-count":44,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2021,4]]}},"alternative-id":["s21072407"],"URL":"https:\/\/doi.org\/10.3390\/s21072407","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2021,3,31]]}}}