Generation of three-dimensional deformation maps from InSAR data using spectral diversity techniques

Erten, Esra; Reigber, Andreas; Hellwich, Olaf

doi:10.1016/j.isprsjprs.2010.04.005

Cited by 34 publications

(16 citation statements)

References 15 publications

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“…The study was hindered by the absence of exclusive validation dataset for accuracy assessment. Our results, however, are in close agreement to the results of previous studies (Fialko et al, 2005;Sarti et al, 2006;Erten et al, 2010;Hu et al, 2010) on the 2003 Bam earthquake. As the Bam earthquake is well studied geologically and with remote sensing data, therefore this deficiency was compensated to some extent.…”

Section: Discussionsupporting

confidence: 93%

“…These four maps can be combined to retrieve the three orthogonal displacement components of a 3D displacement field. In the past they were combined using weighted least squares Pathier et al, 2006;Erten et al, 2010;Hu et al, 2010). The weights were selected by taking the inverse of variance for each displacement map and considering the spatial independence among measurements.…”

Section: Earthquake Monitoringmentioning

confidence: 99%

“…These techniques could not measure coseismic displacements at some locations due to decorrelation of pre-and post-earthquake SAR images. Decorrelation in the images can be caused by several factors such as topography and landuse/landcover changes due to changes in vegetation, water-bodies or human activities Gonzalez et al, 2009;Erten et al, 2010). It can also be introduced by large baseline i.e., perpendicular distance between orbits of the two acquisitions.…”

Section: Reflectionsmentioning

confidence: 99%

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Geostatistical and space geodetic methods on a 3D coseismic displacement field

Yaseen¹

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SummaryCoseismic displacements at the Earth's surface arise due to the release of energy by earthquakes. The energy, and hence the amount of displacement, depends mainly on the magnitude, depth and the movement mechanism of an earthquake. Coseismic displacements play a vital role in the characterization of geological faults and understanding of earthquake dynamics. This understanding is based in turn on the accurate measurement of coseismic displacements and creation of a continuous-field displacement map. The main objective of this research was to measure the coseismic displacements and to construct a 3D coseismic displacement field for an earthquake.Displacements are typically measured by comparing pre-and postearthquake radar images. In this study InSAR is used that measures phase (time) differences between pre-and post-earthquake SAR images resulting in displacements into line-of-sight (LOS) direction. The LOS is mainly sensitive in the vertical direction, moderately in the across flight direction and insensitive in the along flight direction. To measure the along-flight displacement component pixel tracking was used, providing azimuth offsets (AZO). Pixel tracking correlates the coregistered pre-and post-earthquake optical or SAR images. In this study pixel tracking was used for both SAR and optical data, whereas InSAR is used for SAR data only. Using the obtained displacement results, a 3D displacement field was generated from both descending and ascending image pairs bracketing the earthquake event.The study considered the 2003 Bam earthquake. The city is located on a geological fault that caused occurrence of the earthquake. From the measured displacements it appeared that the major part of the fault is located south of the city. It has a length of 13 km and the fault strike equals 170º, close to the along flight direction. The major vertical displacement occurred on east of the fault, showing a uplift in the south-eastern part of displacement map and subsidence in its north-eastern part. Horizontal displacements showed a relative movement along the fault in the along flight direction. The displacement was greater in horizontal direction than the LOS, revealing that the earthquake causative fault is a strike-slip fault.A displacement map commonly contains missing coseismic values due to lack of coherence between SAR images. In this study, missing displacement values were caused by non-coherence of pre-and post-earthquake SAR images for the area affected by the earthquake. These missing displacement values may be important for fault characterization, in particular if they occur along the earthquake-causative geological fault. Missing values were interpolated to evaluate the continuity of the earthquake causative fault underneath the city of Bam. i To assess the accuracy of the interpolated coseismic values, subsets of missing co-seismic values were selected and translated towards locations where displacement values exist. Here, missing values were generated artificially. These values were interpolate...

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Section: Discussionsupporting

confidence: 93%

Section: Earthquake Monitoringmentioning

confidence: 99%

Section: Reflectionsmentioning

confidence: 99%

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Geostatistical and space geodetic methods on a 3D coseismic displacement field

Yaseen¹

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“…Although the interpretation of SAR data is more difficult than that of multispectral imagery, several studies have assessed the positive impact of temporal SAR imaging on change detection applications [3]- [6]. In particular, temporal multichannel change detection techniques are interesting considering their inherent properties which are independent of SAR imaging mode, such as the generalized variance ratio [7] and the maximum-likelihood ratio test (MLRT) [5], [8], [9].…”

mentioning

confidence: 99%

A New Coherent Similarity Measure for Temporal Multichannel Scene Characterization

Erten¹,

Reigber

Ferro-Famil

et al. 2012

IEEE Trans. Geosci. Remote Sensing

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Abstract-This paper proposes a new method for a measure of coherent similarity between temporal multichannel synthetic aperture radar (SAR) images and its implementation to change detection application. The method is based on mutual information (MI) from information theory. The MI measures the amount of information in common between coherent temporal multichannel SAR acquisitions. In order to develop an algorithm for all kinds of SAR images, such as interferometric SAR, polarimetric-interferometric SAR (PolInSAR), and partial PolIn-SAR, first, the joint density function of temporal multichannel images based on their second-order statistics has been derived. Then, the derived joint density function is used to calculate an analytical expression for the MI between temporal images, which is assumed to be maximal if the temporal images are identical. Although, in this paper, a new coherent similarity measure has analytically been derived for temporal polarimetric SAR images based on complex Wishart process in time, since the mathematical formulation is general, it can equally well be implemented into any kind of multivariate remote sensing data, such as multispectral optical and interferometric images after small continuation. This derived quantity has been implemented for change detection application whose aim is to characterize the temporal behavior of the acquisitions. A comparison between the proposed and the other well-known change detection methods by means of scene characterization is shown, describing the advantages due to the fact that the proposed change detector involves almost every facet of applied change detection.

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“…In addition, the use of a combination of an ascending and a descending interferogram overcomes this 3-D problem, but with the cost of an additional data set [7]. The interferometric spectral diversity technique (split-band interferometry) overcomes this problem by supplying the velocity information also in the azimuth direction (perpendicular to the LOS) and was successfully applied with the aim of 3-D motion vector extraction [8], [9]. The method estimates absolute phase-absolute range-by calculating the phase differences of the two interferograms generated from bandpass-filtered images [10], [11].…”

mentioning

confidence: 99%

Glacier Velocity Monitoring by Maximum Likelihood Texture Tracking

Erten

Reigber

Hellwich

et al. 2009

IEEE Trans. Geosci. Remote Sensing

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Abstract-The performance of a tracking algorithm considering remotely sensed data strongly depends on a correct statistical description of the data, i.e., its noise model. The objective of this paper is to introduce a new intensity tracking algorithm for synthetic aperture radar (SAR) data, considering its multiplicative speckle/ noise model. The proposed tracking algorithm is discussed regarding the measurement of glacier velocities. Glacier monitoring exhibits complex spatial and temporal dynamics including snowfall, melting, and ice flows at a variety of spatial and temporal scales. Due to these complex characteristics, most traditional methods based on SAR suffer from speckle decorrelation that results in a low signal-to-noise ratio. The proposed tracking technique improves the accuracy of the classical intensity tracking technique by making use of the temporal speckle structure. Even though a new intensity-based matching algorithm is proposed, particularly for incoherent data sets, the analysis of the proposed technique was also performed for correlated data sets. As it is demonstrated, the velocity monitoring can be continuously performed by using the maximum likelihood (ML) texture tracking without any assumption concerning the correlation of the data set. The ML texture tracking approach was tested on ENVISAT-ASAR data acquired during summer 2004 over the Inyltshik glacier in Kyrgyzstan, representing one of the largest alpine glacier systems of the world. It will be demonstrated that the proposed technique is capable of robustly and precisely detecting the surface velocity field and velocity changes in time.

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Generation of three-dimensional deformation maps from InSAR data using spectral diversity techniques

Cited by 34 publications

References 15 publications

Geostatistical and space geodetic methods on a 3D coseismic displacement field

Geostatistical and space geodetic methods on a 3D coseismic displacement field

A New Coherent Similarity Measure for Temporal Multichannel Scene Characterization

Glacier Velocity Monitoring by Maximum Likelihood Texture Tracking

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