A low-cost landslide displacement activity assessment from time-lapse photogrammetry and rainfall data: Application to the Tessina landslide site

Gabrieli, Fabio; Corain, Livio; Vettore, L.

doi:10.1016/j.geomorph.2016.06.030

Cited by 25 publications

(17 citation statements)

References 39 publications

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“…The processing chain adopted in this study is based upon the approach presented in Dematteis et al [34], and can be divided into four steps. First, certain image enhancement operations are conducted [12][13][14]. The scene illuminant is computed using principal components analysis (PCA) [35] and then subtracted from the trichromatic (RGB) image.…”

Section: Image Cross-correlation Processingmentioning

confidence: 99%

“…This prevents an automated ICC procedure being applied in near-real-time that could be used for warning purposes, as a human intervention to selecting suitable images is required. In addition, the presence of shadows formed by the interaction between direct illumination and surface roughness can cause errors in the cross-correlation (CC) process, and this problem has often been considered in literature [1,[11][12][13][14][15][16]. The most popular solution used to avoid such effects is to consider pictures that have a similar visual appearance, and have been acquired in a similar light (usually in the middle of the day [12,17,18] when the length of shadows is reduced, due to the higher sun elevation angle).…”

Section: Introductionmentioning

confidence: 99%

“…Both of these approaches involve the manual selection of images, and images with a partial or absent view (caused by fog or the presence of obstacles), and with non-homogeneous illumination, are thus discarded. In this respect, Gabrieli et al [14] developed a method for automatically discarding images taken during adverse meteorological conditions; this involved analysing the mean and standard deviation of colour values along predefined lines in the image. Hadhri et al [19] also automatically identified images containing artefacts, or where vision was obscured, using a posteriori statistical analysis based on image entropy.…”

Section: Introductionmentioning

confidence: 99%

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Image Classification for Automated Image Cross-Correlation Applications in the Geosciences

2019

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In Earth Science, image cross-correlation (ICC) can be used to identify the evolution of active processes. However, this technology can be ineffective, because it is sometimes difficult to visualize certain phenomena, and surface roughness can cause shadows. In such instances, manual image selection is required to select images that are suitably illuminated, and in which visibility is adequate. This impedes the development of an autonomous system applied to ICC in monitoring applications. In this paper, the uncertainty introduced by the presence of shadows is quantitatively analysed, and a method suitable for ICC applications is proposed: The method automatically selects images, and is based on a supervised classification of images using the support vector machine. According to visual and illumination conditions, the images are divided into three classes: (i) No visibility, (ii) direct illumination and (iii) diffuse illumination. Images belonging to the diffuse illumination class are used in cross-correlation processing. Finally, an operative procedure is presented for applying the automated ICC processing chain in geoscience monitoring applications.

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Section: Image Cross-correlation Processingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Image Classification for Automated Image Cross-Correlation Applications in the Geosciences

2019

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“…Images captured in a pre-defined interval are used to perform change analysis, e.g. for snow cover monitoring (Parajka et al, 2012), identification of frost creep processes (Matsuoka, 2014), gully headcut retreat and piping observations (Nichols et al, 2016), land slide tracking (Gabrieli et al, 2016), or to monitor wood loads of rivers (Kramer and Wohl, 2014). Martin-Brualla et al (2015) mine internet image collections and achieve time-lapse sequences of inter alia geomorphic changes and thus enable continuous process monitoring to a certain degree depending on available image quality.…”

Section: Introductionmentioning

confidence: 99%

Time lapse structure‐from‐motion photogrammetry for continuous geomorphic monitoring

Eltner

Kaiser

Abellán

et al. 2017

Earth Surf Processes Landf

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Recent advances are made in earth surface reconstruction with high spatial resolution due to SfM photogrammetry. High flexibility of data acquisition and high potential of process automation allows for a significant increase of the temporal resolution, as well, which is especially interesting to assess geomorphic changes. Two case studies are presented where 4D reconstruction is performed to study soil surface changes at 15 seconds intervals: (a) a thunderstorm event is captured at field scale and (b) a rainfall simulation is observed at plot scale. A workflow is introduced for automatic data acquisition and processing including the following approach: data collection, camera calibration and subsequent image correction, template matching to automatically identify ground control points in each image to account for camera movements, 3D reconstruction of each acquisition interval, and finally applying temporal filtering to the resulting surface change models to correct random noise and to increase the reliability of the measurement of signals of change with low intensity. Results reveal surface change detection with cm‐ to mm‐accuracy. Significant soil changes are measured during the events. Ripple and pool sequences become obvious in both case studies. Additionally, roughness changes and hydrostatic effects are apparent along the temporal domain at the plot scale. 4D monitoring with time‐lapse SfM photogrammetry enables new insights into geomorphic processes due to a significant increase of temporal resolution. Copyright © 2017 John Wiley & Sons, Ltd.

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“…Manconi et al (2013) proposed an estimate of the 3D deformation by spatially interpolating over a DEM the punctual data acquired by a total station. Other works projected the data acquired by GBSAR (Dematteis et al, 2017;Luzi et al, 2007), satellite SAR (Huang and Li, 2011) or produced through image cross-correlation (ICC) (Gabrieli et al, 2016;Giordan et al, 2016) along the expected maximum gradient direction or the expected flow direction (Schwalbe and Maas, 2017). Similarly, Ahn and Box (2010), Messerli and Grinsted (2015), and Travelletti et al (2012) applied the image orthorectification and consequently adjusted the ground sampling distance (GSD) of each pixel according to the incident angle.…”

Section: Introductionmentioning

confidence: 99%

4D surface kinematics monitoring through terrestrial radar interferometry and image cross-correlation coupling

Dematteis

Giordan

Zucca

et al. 2018

ISPRS Journal of Photogrammetry and Remote Sensing

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Complex gravitational phenomena can require terrestrial remote sensing solutions for monitoring their possible evolution, especially when in situ installations are not possible. This study merges terrestrial radar interferometry (TRI) and image cross-correlation (ICC) processing, which can detect complementary motion components, to obtain a 3-dimensional system able to measure the actual surface motion field of a pre-defined target. The coupling can be carried out on data acquired from different installations of the devices, and by applying specific transformations of the related coordinate systems. The data georeferencing is a critical issue that affects the correct spatial correspondence of the data and a new approach for georeferencing radar data is proposed. The result is a spatio-temporal (3 + 1-dimensional) high-resolution representation of the surface kinematics. The presented method has been tested for the measurement of the Planpicieux glacier surface kinematics (NW of Italy). The error analysis revealed a millimeter accuracy and precision of the measurement and a georeferencing uncertainty of a few metres.

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A low-cost landslide displacement activity assessment from time-lapse photogrammetry and rainfall data: Application to the Tessina landslide site

Cited by 25 publications

References 39 publications

Image Classification for Automated Image Cross-Correlation Applications in the Geosciences

Image Classification for Automated Image Cross-Correlation Applications in the Geosciences

Time lapse structure‐from‐motion photogrammetry for continuous geomorphic monitoring

4D surface kinematics monitoring through terrestrial radar interferometry and image cross-correlation coupling

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