Next Article in Journal
High-Resolution SAR Image Classification Using Multi-Scale Deep Feature Fusion and Covariance Pooling Manifold Network
Next Article in Special Issue
Targeted Rock Slope Assessment Using Voxels and Object-Oriented Classification
Previous Article in Journal
On the Generalization Ability of Data-Driven Models in the Problem of Total Cloud Cover Retrieval
Previous Article in Special Issue
Multi-Temporal Satellite Interferometry for Fast-Motion Detection: An Application to Salt Solution Mining
Open AccessFeature PaperArticle

Comparison of Digital Image Correlation Methods and the Impact of Noise in Geoscience Applications

Research Institute for Geo-Hydrological Protection, National Research Council of Italy, Strada delle Cacce, 73, 10135 Turin, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2021, 13(2), 327; https://doi.org/10.3390/rs13020327
Received: 30 November 2020 / Revised: 7 January 2021 / Accepted: 14 January 2021 / Published: 19 January 2021
(This article belongs to the Special Issue Remote Sensing Analysis of Geologic Hazards)
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 Bodélé 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. View Full-Text
Keywords: digital image correlation; template matching; natural hazards; surface deformations; optical remote sensing; time-lapse camera digital image correlation; template matching; natural hazards; surface deformations; optical remote sensing; time-lapse camera
Show Figures

Graphical abstract

MDPI and ACS Style

Dematteis, N.; Giordan, D. Comparison of Digital Image Correlation Methods and the Impact of Noise in Geoscience Applications. Remote Sens. 2021, 13, 327. https://doi.org/10.3390/rs13020327

AMA Style

Dematteis N, Giordan D. Comparison of Digital Image Correlation Methods and the Impact of Noise in Geoscience Applications. Remote Sensing. 2021; 13(2):327. https://doi.org/10.3390/rs13020327

Chicago/Turabian Style

Dematteis, Niccolò; Giordan, Daniele. 2021. "Comparison of Digital Image Correlation Methods and the Impact of Noise in Geoscience Applications" Remote Sens. 13, no. 2: 327. https://doi.org/10.3390/rs13020327

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop