{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T18:34:12Z","timestamp":1772217252169,"version":"3.50.1"},"reference-count":33,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2021,1,19]],"date-time":"2021-01-19T00:00:00Z","timestamp":1611014400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Digital image correlation (DIC) is a commonly-adopted technique in geoscience and natural hazard studies to measure the surface deformation of various geophysical phenomena. In the last decades, several different correlation functions have been developed. Additionally, some authors have proposed applying DIC to other image representations, such as image gradients or orientation. Many works have shown the reliability of specific methods, but they have been rarely compared. In particular, a formal analysis of the impact of different sources of noise is missing. Using synthetic images, we analysed 15 different combinations of correlation functions and image representations and we investigated their performances with respect to the presence of 13 noise sources. Besides, we evaluated the influence of the size of the correlation template. We conducted the analysis also on terrestrial photographs of the Planpincieux Glacier (Italy) and Sentinel 2B images of the Bod\u00e9l\u00e9 Depression (Chad). We observed that frequency-based methods are in general less robust against noise, in particular against blurring and speckling, and they tend to underestimate the displacement value. Zero-mean normalised cross-correlation applied to image intensity showed high-quality results. However, it suffers variations of the shadow pattern. Finally, we developed an original similarity function (DOT) that proved to be quite resistant to every noise source.<\/jats:p>","DOI":"10.3390\/rs13020327","type":"journal-article","created":{"date-parts":[[2021,1,20]],"date-time":"2021-01-20T03:34:25Z","timestamp":1611113665000},"page":"327","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":29,"title":["Comparison of Digital Image Correlation Methods and the Impact of Noise in Geoscience Applications"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9232-7531","authenticated-orcid":false,"given":"Niccol\u00f2","family":"Dematteis","sequence":"first","affiliation":[{"name":"Research Institute for Geo-Hydrological Protection, National Research Council of Italy, Strada delle Cacce, 73, 10135 Turin, Italy"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0136-2436","authenticated-orcid":false,"given":"Daniele","family":"Giordan","sequence":"additional","affiliation":[{"name":"Research Institute for Geo-Hydrological Protection, National Research Council of Italy, Strada delle Cacce, 73, 10135 Turin, Italy"}]}],"member":"1968","published-online":{"date-parts":[[2021,1,19]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"73","DOI":"10.1080\/10255842.2012.670855","article-title":"Medical image registration: A review","volume":"17","author":"Oliveira","year":"2014","journal-title":"Comput. Methods Biomech. Biomed. Engin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1379","DOI":"10.1088\/0957-0233\/8\/12\/002","article-title":"Fundamentals of digital particle image velocimetry","volume":"8","author":"Westerweel","year":"1997","journal-title":"Meas. Sci. Technol."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"181","DOI":"10.1007\/BF00190388","article-title":"Digital particle image velocimetry","volume":"10","author":"Willert","year":"1991","journal-title":"Exp. Fluids"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"232","DOI":"10.1007\/BF02325092","article-title":"Applications of digital-image-correlation techniques to experimental mechanics","volume":"25","author":"Chu","year":"1985","journal-title":"Exp. Mech."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"1064","DOI":"10.1109\/36.841985","article-title":"Glacier surface motion computation from digital image s\u00e9quences","volume":"38","author":"Evans","year":"2000","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1029\/2008EO010001","article-title":"Monitoring earth surface dynamics with optical imagery","volume":"89","author":"Leprince","year":"2008","journal-title":"EOS"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Giordan, D., Allasia, P., Dematteis, N., Dell\u2019Anese, F., Vagliasindi, M., and Motta, E. (2016). A low-cost optical remote sensing application for glacier deformation monitoring in an alpine environment. Sensors, 17.","DOI":"10.3390\/s16101750"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1680\/wama.2010.163.5.247","article-title":"Discharge estimation in small irregular river using LSPIV","volume":"163","author":"Sun","year":"2010","journal-title":"Proc. Inst. Civ. Eng. Water Manag."},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Fujita, I., and Aya, S. (August, January 30). Refinement of LSPIV technique for monitoring river surface flows. Proceedings of the Joint Conference on Water Resource Engineering and Water Resources Planning and Management 2000: Building Partnerships, 2004, Minneapolis, MN, USA.","DOI":"10.1061\/40517(2000)312"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.geomorph.2016.06.030","article-title":"A low-cost landslide displacement activity assessment from time-lapse photogrammetry and rainfall data: Application to the Tessina landslide site","volume":"269","author":"Gabrieli","year":"2016","journal-title":"Geomorphology"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"39","DOI":"10.1016\/j.isprsjprs.2012.03.007","article-title":"Correlation of multi-temporal ground-based optical images for landslide monitoring: Application, potential and limitations","volume":"70","author":"Travelletti","year":"2012","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"723","DOI":"10.3189\/002214310793146313","article-title":"Glacier velocities from time-lapse photos: Technique development and first results from the Extreme Ice Survey (EIS) in Greenland","volume":"56","author":"Ahn","year":"2010","journal-title":"J. Glaciol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.isprsjprs.2018.05.017","article-title":"4D surface kinematics monitoring through terrestrial radar interferometry and image cross-correlation coupling","volume":"142","author":"Dematteis","year":"2018","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1096","DOI":"10.1007\/s00348-005-0016-6","article-title":"Universal outlier detection for PIV data","volume":"39","author":"Westerweel","year":"2005","journal-title":"Exp. Fluids"},{"key":"ref_15","unstructured":"Brinkerhoff, D., and O\u2019Neel, S. (2017). Velocity variations at Columbia Glacier captured by particle filtering of oblique time-lapse images. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.isprsjprs.2019.02.007","article-title":"Time-lapse optical flow regularization for geophysical complex phenomena monitoring","volume":"150","author":"Hadhri","year":"2019","journal-title":"ISPRS J. Photogramm. Remote Sens."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Dematteis, N., Giordan, D., and Allasia, P. (2019). Image Classification for Automated Image Cross-Correlation Applications in the Geosciences. Appl. Sci., 9.","DOI":"10.3390\/app9112357"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"339","DOI":"10.1016\/j.rse.2011.11.024","article-title":"Evaluation of existing image matching methods for deriving glacier surface displacements globally from optical satellite imagery","volume":"118","author":"Heid","year":"2012","journal-title":"Remote Sens. Environ."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bickel, V.T., Manconi, A., and Amann, F. (2018). Quantitative assessment of digital image correlation methods to detect and monitor surface displacements of large slope instabilities. Remote Sens., 10.","DOI":"10.3390\/rs10060865"},{"key":"ref_20","unstructured":"Martin, J., and Crowley, J.L. (1995). Comparison of Correlation Techniques. Proceedings of the Intelligent Autonomous Systems, IOS Press."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1007\/s003480050005","article-title":"A comparative study of the MQD method and several correlation-based PIV evaluation algorithms","volume":"28","author":"Merzkirch","year":"2000","journal-title":"Exp. Fluids"},{"key":"ref_22","unstructured":"Pust, O. (2000, January 10\u201313). PIV: Direct cross-correlation compared with FFT-based cross-correlation. Proceedings of the 10th International Symposium on Applications of Laser Techniques to Fluid Mechanics, Lisbon, Portugal."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1529","DOI":"10.1109\/TGRS.2006.888937","article-title":"Automatic and precise orthorectification, coregistration, and subpixel correlation of satellite images, application to ground deformation measurements","volume":"45","author":"Leprince","year":"2007","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1364\/OL.33.000156","article-title":"Efficient subpixel image registration algorithms","volume":"33","author":"Thurman","year":"2008","journal-title":"Opt. Lett."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Sj\u00f6dahl, M. (2019). Gradient correlation functions in digital image correlation. Appl. Sci., 9.","DOI":"10.3390\/app9102127"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Fitch, A.J., Kadyrov, A., Christmas, W.J., and Kittler, J. (2002, January 2\u20135). Orientation Correlation. Proceedings of the British Machine Vision Conference, Cardiff, UK.","DOI":"10.5244\/C.16.11"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1007\/BF00189049","article-title":"Method of tracking ensembles of particle images","volume":"21","author":"Gui","year":"1996","journal-title":"Exp. Fluids"},{"key":"ref_28","first-page":"163","article-title":"The phase correlation image alignment method","volume":"6","author":"Kuglin","year":"1975","journal-title":"IEEE Int. Conf. Cybern. Soc."},{"key":"ref_29","first-page":"120","article-title":"Fast normalized cross-correlation","volume":"10","author":"Lewis","year":"1995","journal-title":"Vis. Interface"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"e30","DOI":"10.5334\/jors.bl","article-title":"PIVlab\u2013Towards User-friendly, Affordable and Accurate Digital Particle Image Velocimetry in MATLAB","volume":"2","author":"Thielicke","year":"2014","journal-title":"J. Open Res. Softw."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"188","DOI":"10.1017\/jog.2019.99","article-title":"Classification and kinematics of the Planpincieux Glacier break-offs using photographic time-lapse analysis","volume":"66","author":"Giordan","year":"2020","journal-title":"J. Glaciol."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Singh, P., and Shree, R. (October, January 30). Analysis and effects of speckle noise in SAR images. Proceedings of the 2016 International Conference on Advances in Computing, Communication and Automation (Fall), ICACCA 2016, Bareilly, India.","DOI":"10.1109\/ICACCAF.2016.7748978"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Baird, T., Bristow, C.S., and Vermeesch, P. (2019). Measuring sand dune migration rates with COSI-Corr and landsat: Opportunities and challenges. Remote Sens., 11.","DOI":"10.3390\/rs11202423"}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/2\/327\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:13:05Z","timestamp":1760159585000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/2\/327"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,1,19]]},"references-count":33,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2021,1]]}},"alternative-id":["rs13020327"],"URL":"https:\/\/doi.org\/10.3390\/rs13020327","relation":{},"ISSN":["2072-4292"],"issn-type":[{"value":"2072-4292","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,1,19]]}}}