Radar-Coding and Geocoding Lookup Tables for the Fusion of GIS and SAR Data in Mountain Areas

Pétillot, Ivan; Trouvé, Emmanuël; Bolon, Philippe; Julea, Andreea; Yan, Yajing; Gay, Michel; Vanpe, Jean-Michel

doi:10.1109/lgrs.2009.2034118

Cited by 18 publications

(8 citation statements)

References 9 publications

(7 reference statements)

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“…This geo-physical model is used to simulate the 3D (East, North, Up) surface displacement firstly in the ground geometry. Then the LOS displacement is computed according to the local incidence angle and projected in the SAR geometry by using a DTM and radar-coding tools [9]. An example of the a priori information of displacement and the associated scale image is shown in Fig.…”

Section: Application To the 2005 Kashmir Earthquakementioning

confidence: 99%

Fusion of prior information and multi-scales local frequencies to facilitate D-InSAR phase unwrapping

Yan¹,

Trouvé²,

Pinel³

2012

2012 IEEE International Geoscience and Remote Sensing Symposium

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In this paper, a dedicated phase unwrapping approach, taking a priori information into account and combining multi-scales local frequencies of the interferometric phase, is developed in order to get around of the discontinuity and aliasing problems. In this approach, the interferogram is characterized by local frequency of the phase. The multi-scales local frequencies of the interferometric phase are estimated and fused to the local frequency at the optimal scale. This optimal scale, the lowest resolution scale allowing phase unwrapping without aliasing problem, is determined from the a priori displacement information. The application is performed on the displacement measurement of the 2005 Kashmir earthquake. The a priori displacement information is issued from a deformation model. The advantages of this approach are highlighted by the obtained results.

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Section: Application To the 2005 Kashmir Earthquakementioning

confidence: 99%

Fusion of prior information and multi-scales local frequencies to facilitate D-InSAR phase unwrapping

Yan¹,

Trouvé²,

Pinel³

2012

2012 IEEE International Geoscience and Remote Sensing Symposium

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“…• Projecting a radar image on the ground introduces artefacts, because of the special SAR geometric configuration [34]. These artifacts are further amplified for high relief terrains, and also when the incidence angle variation is great, such as encountered for our airborne acquisitions.…”

Section: B Tested Co-registration Scenariosmentioning

confidence: 99%

Adaptation and Evaluation of an Optical Flow Method Applied to Coregistration of Forest Remote Sensing Images

Brigot

Colin-Koeniguer

Plyer

et al. 2016

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The coregistration of heterogeneous geospatial images is useful in various remote sensing applications. Since the number of available data increases and the resolution improves, it is interesting to have an approach as automated, fast, robust and accurate as possible. In this paper, we present a solution based on optical-flow computation. This algorithm called GeFolki allows the registration of images in a non-parametric and dense way. GeFolki is based on a local method of optical flow derived from the Lucas-Kanade algorithm, with a multi-scale implementation, and a specific filtering including rank filtering, rolling guidance filtering and local contrast inversion. The efficiency of our coregistration chain is shown on radar, LIDAR and optical images on Remningstorp forest in Sweden. An analysis of the relevant parameters is investigated for several scenarios. Finally, we demonstrate the accuracy of our coregistration by proposing specific metrics for LIDAR/radar coregistration, and optics/radar coregistration.

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“…It is dedicated to the processing of optical and SAR images for the measurement of surface displacement by offset tracking (Ponton et al, 2014;Benoit et al, 2015). The proposed pipeline for SAR images is based on the use of an accurate DEM and the orbital and sensor information to perform the following steps: 1) the geocoding (latitude, longitude, and elevation) of the pixels of the SAR images and the "radarcoding" (range and azimuth coordinates) of the pixels of the DEM grid in different SAR images (Petillot et al, 2010); 2) the crop of the different SAR images based on the min-max range and azimuth coordinates for the studied area, which provides a rough coregistration of the SAR images without any resampling; 3) the computation of range and azimuth offset maps by crosscorrelation between these roughly coregistered SAR images. Subpixel offsets are measured by a fast implementation of the normalized cross-correlation in the spatial domain followed by a parabolic interpolation of the correlation pick (Vernier et al, 2011).…”

Section: Efidir Toolsmentioning

confidence: 99%

“…This second step can be carried out using multiple approaches such as offset tracking (Wegmuller et al, 1998), the analysis of the linear phase component of the SAR PSF (Scheiber and Moreira, 2000) or optical flow (Plyer et al, 2015). In order to perform finer coregistration without these two steps, it is possible to perform a DEM-assisted coregistration as for example in Petillot et al (2010) or in Nitti et al (2008). However, having a fine up-to-date DEM can be complicated, mostly in high and low latitudes, which are not covered by SRTM acquisition and more difficult to image with optical sensors or because changes such as glacier melt make the DEM quickly obsolete (Altena and Kääb, 2017).…”

Section: Introductionmentioning

confidence: 99%

LabSAR, a one-GCP coregistration tool for SAR–InSAR local analysis in high-mountain regions

Weissgerber¹,

Charrier²,

Thomas³

et al. 2022

Front. Remote Sens.

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The coregistration of single-look complex (SLC) SAR images for InSAR or offset tracking applications is often performed by using an accurate DEM and precise orbital information. However, in cold regions, such DEMs are rare over high-latitude areas or not up-to-date over fast melting glaciers for instance. To overcome this difficulty, we propose in this article a coregistration method preserving InSAR phase information that only requires a 3D point of reference instead of a full DEM. Developed in a Python toolbox called LabSAR, the proposed method only uses orbital information to coregister the images on the sphere centered on the Earth center passing by the ground control point (GCP). Thanks to the use of the orbital information, the so-called orbital fringes are compensated without having to estimate them. This coregistration method is compared to other approaches in two different types of applications, InSAR and offset tracking, on a PAZ Dual-Pol Temporal Stack covering the Mont Blanc massif (western European Alps). First, InSAR measurements from LabSAR are compared with the results of the Sentinel-1 ESA toolbox (SNAP). The LabSAR interferograms exhibit clearer topographical fringes, with fewer parameters to set. Second, offset tracking based on LabSAR coregistated images is used to measure the displacement of the Bossons glacier. The results are compared with those obtained by a conventional approach developed in the EFIDIR tools. By evaluating the uncertainties of both approaches using displacements over stable areas and the temporal closure error, similar uncertainty values are found. However, velocity values differ between the two approaches, especially in areas where the altitudes are different from the altitude of the reference point. The difference can reach up to 0.06 m/day, which is in the range of the glacier velocity measurement uncertainty given in the literature. The impact of the altitude of the reference point is limited: this single GCP can be chosen at the median altitude of the study area. The error margin on the knowledge of this altitude is 1,000 m, which is sufficient for the altitude to be considered as known for a wide range of study area in the world.

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Radar-Coding and Geocoding Lookup Tables for the Fusion of GIS and SAR Data in Mountain Areas

Cited by 18 publications

References 9 publications

Fusion of prior information and multi-scales local frequencies to facilitate D-InSAR phase unwrapping

Fusion of prior information and multi-scales local frequencies to facilitate D-InSAR phase unwrapping

Adaptation and Evaluation of an Optical Flow Method Applied to Coregistration of Forest Remote Sensing Images

LabSAR, a one-GCP coregistration tool for SAR–InSAR local analysis in high-mountain regions

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