Locally adaptive template sizes for matching repeat images of Earth surface mass movements

Debella-Gilo, Misganu; Kääb, Andreas

doi:10.1016/j.isprsjprs.2012.02.002

Cited by 42 publications

(31 citation statements)

References 25 publications

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“…Stable grounds are, however, not a complete representation for the mass movements as one source of error, which is the deformation, does strictly speaking not exist on the stable ground. Thus, to compare the performance of the algorithm over the moving masses to that of the NCC, the SNR of reconstructing the reference image from the search image is computed as detailed in [39], assuming that accurate matching leads to accurate reconstruction. The SNR is computed as the ratio between the correlation coefficient (ρ) and 1 − ρ.…”

Section: Performance Evaluationmentioning

confidence: 99%

Measurement of Surface Displacement and Deformation of Mass Movements Using Least Squares Matching of Repeat High Resolution Satellite and Aerial Images

Debella-Gilo

Kääb

2012

Remote Sensing

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Displacement and deformation are fundamental measures of Earth surface mass movements such as glacier flow, rockglacier creep and rockslides. Ground-based methods of monitoring such mass movements can be costly, time consuming and limited in spatial and temporal coverage. Remote sensing techniques, here matching of repeat optical images, are increasingly used to obtain displacement and deformation fields. Strain rates are usually computed in a post-processing step based on the gradients of the measured velocity field. This study explores the potential of automatically and directly computing velocity, rotation and strain rates on Earth surface mass movements simultaneously from the matching positions and the parameters of the geometric transformation models using the least squares matching (LSM) approach. The procedures are exemplified using bi-temporal high resolution satellite and aerial images of glacier flow, rockglacier creep and land sliding. The results show that LSM matches the images and computes longitudinal strain rates, transverse strain rates and shear strain rates reliably with mean absolute deviations in the order of 10 −4 (one level of significance below the measured values) as evaluated on stable grounds. The LSM also improves the accuracy of displacement estimation of the pixel-precision normalized cross-correlation by over 90% under ideal (simulated) circumstances and by about 25% for real multi-temporal images of mass movements. OPEN ACCESSRemote Sens. 2012, 4 44

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Section: Performance Evaluationmentioning

confidence: 99%

Measurement of Surface Displacement and Deformation of Mass Movements Using Least Squares Matching of Repeat High Resolution Satellite and Aerial Images

Debella-Gilo

Kääb

2012

Remote Sensing

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“…Visualization-based qualitative evaluation is a solution for identifying observable gross errors; however, more quantitative error measurements/metrics for displacement vectors are desired [28]. Debella-Gilo and Kääb [21] devised a method that assumed the Earth surface entities retain their radiometric characteristics after displacement; thus, when comparing the intensities of pixels at the original In a change-analysis study, an accuracy assessment is typically performed in order to evaluate the performance of a given change-analysis method. A conventional method consists of utilization of per-pixel ground-reference data for the purpose of constructing an error matrix.…”

Section: Land-change Analysismentioning

confidence: 99%

“…Experiments involving synthetic images found that, if images that are separated by 6 h are available, such error may be decreased to approximately 0.10-0.15 m·s −1 [27].) For glacier tracking via the MCC method, comparisons between MCC displacement vectors and actual displacements are not likely, as true displacement data are not usually available for such moving masses [21]. Visualization-based qualitative evaluation is a solution for identifying observable gross errors; however, more quantitative error measurements/metrics for displacement vectors are desired [28].…”

Section: Land-change Analysismentioning

confidence: 99%

“…Visualization-based qualitative evaluation is a solution for identifying observable gross errors; however, more quantitative error measurements/metrics for displacement vectors are desired [28]. Debella-Gilo and Kääb [21] devised a method that assumed the Earth surface entities retain their radiometric characteristics after displacement; thus, when comparing the intensities of pixels at the original location and those after displacement in the second image, the difference should be low if the displacement is genuine. However, such a method is not likely to work for general land-cover change, as the algorithm is not necessarily tracking any specific entity; thus, the assumption of radiometric retention or similarity does not hold.…”

Section: Land-change Analysismentioning

confidence: 99%

“…Furthermore, there is only limited prior work on determining the influence of different MCC parameter values on the resultant change vectors [21]. More thorough sensitivity analyses are needed, particularly when MCC is applied to terrestrial environments, given the absence of any such prior studies.…”

Section: Introductionmentioning

confidence: 99%

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What is the Direction of Land Change? A New Approach to Land-Change Analysis

et al. 2017

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Accurate characterization of the direction of land change is a neglected aspect of land dynamics. Knowledge on direction of historical land change can be useful information when understanding relative influence of different land-change drivers is of interest. In this study, we present a novel perspective on land-change analysis by focusing on directionality of change. To this end, we employed Maximum Cross-Correlation (MCC) approach to estimate the directional change in land cover in a dynamic river floodplain environment using Landsat 5 Thematic Mapper (TM) images. This approach has previously been used for detecting and measuring fluid and ice motions but not to study directional changes in land cover. We applied the MCC approach on land-cover class membership layers derived from fuzzy remote-sensing image classification. We tested the sensitivity of the resulting displacement vectors to three user-defined parameters-template size, search window size, and a threshold parameter to determine valid (non-noisy) displacement vectors-that directly affect the generation of change, or displacement, vectors; this has not previously been thoroughly investigated in any application domain. The results demonstrate that it is possible to quantitatively measure the rate of directional change in land cover in this floodplain environment using this particular approach. Sensitivity analyses indicate that template size and MCC threshold parameter are more influential on the displacement vectors than search window size. The results vary by land-cover class, suggesting that spatial configuration of land-cover classes should be taken into consideration in the implementation of the method.

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