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A quantitative assessment of the SBAS algorithm performance for surface deformation retrieval from DInSAR data [An article from: Remote Sensing of Environment]
Book Details
Author(s)F. Casu, M. Manzo, R. Lanari
PublisherElsevier
ISBN / ASINB000RR8GSO
ISBN-13978B000RR8GS8
AvailabilityAvailable for download now
MarketplaceUnited States 🇺🇸
Description
This digital document is a journal article from Remote Sensing of Environment, published by Elsevier in 2006. The article is delivered in HTML format and is available in your Amazon.com Media Library immediately after purchase. You can view it with any web browser.
Description:
We investigate in this work the performance of the Small BAseline Subset (SBAS) approach that is a Differential Synthetic Aperture Radar Interferometry (DInSAR) algorithm allowing the generation of mean deformation velocity maps and displacement time series from a data set of subsequently acquired SAR images. In particular, we have carried out a quantitative assessment of the SBAS procedure performance by processing SAR data acquired by the European Remote Sensing Satellite (ERS) sensors and comparing the achieved results with geodetic measurements that are assumed as reference. The analysis has been focused on the Napoli bay (Italy) and Los Angeles (California) test areas where different deformation phenomena are present and, at the same time, a large amount of ERS SAR data is available as well as geometric leveling (in the Napoli zone) and continuous GPS (in the Los Angeles zone) measurements, to be used for our performance analysis. Moreover, due to the presence of large urbanized zones, the selected test sites are also characterized by extended, highly coherent areas in the DInSAR maps. The presented study shows that the SBAS technique provides an estimate of the mean deformation velocity with a standard deviation of about 1 mm/year for a typical ERS data set including between 40 and 60 images. Moreover, the single displacement measurements, computed with respect to a reference point of known motion, show a sub-centimetric accuracy with a standard deviation of about 5 mm, consistently in both the SAR/leveling and SAR/GPS comparisons; we also show that there is an increase of this standard deviation value as we move away from the reference SAR pixel, with an estimated spatial variation value of about 0.05 mm/km.
Description:
We investigate in this work the performance of the Small BAseline Subset (SBAS) approach that is a Differential Synthetic Aperture Radar Interferometry (DInSAR) algorithm allowing the generation of mean deformation velocity maps and displacement time series from a data set of subsequently acquired SAR images. In particular, we have carried out a quantitative assessment of the SBAS procedure performance by processing SAR data acquired by the European Remote Sensing Satellite (ERS) sensors and comparing the achieved results with geodetic measurements that are assumed as reference. The analysis has been focused on the Napoli bay (Italy) and Los Angeles (California) test areas where different deformation phenomena are present and, at the same time, a large amount of ERS SAR data is available as well as geometric leveling (in the Napoli zone) and continuous GPS (in the Los Angeles zone) measurements, to be used for our performance analysis. Moreover, due to the presence of large urbanized zones, the selected test sites are also characterized by extended, highly coherent areas in the DInSAR maps. The presented study shows that the SBAS technique provides an estimate of the mean deformation velocity with a standard deviation of about 1 mm/year for a typical ERS data set including between 40 and 60 images. Moreover, the single displacement measurements, computed with respect to a reference point of known motion, show a sub-centimetric accuracy with a standard deviation of about 5 mm, consistently in both the SAR/leveling and SAR/GPS comparisons; we also show that there is an increase of this standard deviation value as we move away from the reference SAR pixel, with an estimated spatial variation value of about 0.05 mm/km.
