The Parallel SBAS Approach for Sentinel-1 Interferometric Wide Swath Deformation Time-Series Generation: Algorithm Description and Products Quality Assessment
We present an advanced differential synthetic aperture radar (SAR) interferometry (DInSAR) processing chain, based on the Parallel Small BAseline Subset (P-SBAS) technique, for the efficient generation of deformation time series from Sentinel-1 (S-1) interferometric wide (IW) swath SAR data sets. We...
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creator | Manunta, Michele De Luca, Claudio Zinno, Ivana Casu, Francesco Manzo, Mariarosaria Bonano, Manuela Fusco, Adele Pepe, Antonio Onorato, Giovanni Berardino, Paolo De Martino, Prospero Lanari, Riccardo |
description | We present an advanced differential synthetic aperture radar (SAR) interferometry (DInSAR) processing chain, based on the Parallel Small BAseline Subset (P-SBAS) technique, for the efficient generation of deformation time series from Sentinel-1 (S-1) interferometric wide (IW) swath SAR data sets. We first discuss an effective solution for the generation of high-quality interferograms, which properly accounts for the peculiarities of the terrain observation with progressive scans (TOPS) acquisition mode used to collect S-1 IW SAR data. These data characteristics are also properly accounted within the developed processing chain, taking full advantage from the burst partitioning. Indeed, such data structure represents a key element in the proposed P-SBAS implementation of the S-1 IW processing chain, whose migration into a cloud computing (CC) environment is also envisaged. An extensive experimental analysis, which allows us to assess the quality of the obtained interferometric products, is presented. To do this, we apply the developed S-1 IW P-SBAS processing chain to the overall archive acquired from descending orbits during the March 2015-April 2017 time span over the whole Italian territory, consisting in 2740 S-1 slices. In particular, the quality of the final results is assessed through a large-scale comparison with the GPS measurements relevant to nearly 500 stations. The mean standard deviation value of the differences between the DInSAR and the GPS time series (projected in the radar line of sight) is less than 0.5 cm, thus confirming the effectiveness of the implemented solution. Finally, a discussion about the performance achieved by migrating the developed processing chain within the Amazon Web Services CC environment is addressed, highlighting that a two-year data set relevant to a standard S-1 IW slice can be reliably processed in about 30 h.The presented results demonstrate the capability of the implemented P-SBAS approach to efficiently and effectively process large S-1 IW data sets relevant to extended portions of the earth surface, paving the way to the systematic generation of advanced DInSAR products to monitor ground displacements at a very wide spatial scale. |
doi_str_mv | 10.1109/TGRS.2019.2904912 |
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We first discuss an effective solution for the generation of high-quality interferograms, which properly accounts for the peculiarities of the terrain observation with progressive scans (TOPS) acquisition mode used to collect S-1 IW SAR data. These data characteristics are also properly accounted within the developed processing chain, taking full advantage from the burst partitioning. Indeed, such data structure represents a key element in the proposed P-SBAS implementation of the S-1 IW processing chain, whose migration into a cloud computing (CC) environment is also envisaged. An extensive experimental analysis, which allows us to assess the quality of the obtained interferometric products, is presented. To do this, we apply the developed S-1 IW P-SBAS processing chain to the overall archive acquired from descending orbits during the March 2015-April 2017 time span over the whole Italian territory, consisting in 2740 S-1 slices. In particular, the quality of the final results is assessed through a large-scale comparison with the GPS measurements relevant to nearly 500 stations. The mean standard deviation value of the differences between the DInSAR and the GPS time series (projected in the radar line of sight) is less than 0.5 cm, thus confirming the effectiveness of the implemented solution. Finally, a discussion about the performance achieved by migrating the developed processing chain within the Amazon Web Services CC environment is addressed, highlighting that a two-year data set relevant to a standard S-1 IW slice can be reliably processed in about 30 h.The presented results demonstrate the capability of the implemented P-SBAS approach to efficiently and effectively process large S-1 IW data sets relevant to extended portions of the earth surface, paving the way to the systematic generation of advanced DInSAR products to monitor ground displacements at a very wide spatial scale.</description><identifier>ISSN: 0196-2892</identifier><identifier>EISSN: 1558-0644</identifier><identifier>DOI: 10.1109/TGRS.2019.2904912</identifier><identifier>CODEN: IGRSD2</identifier><language>eng</language><publisher>New York: IEEE</publisher><subject>Algorithms ; Archives & records ; Chains ; Cloud computing ; Cloud computing (CC) ; Data ; Data structures ; Datasets ; Deformation ; deformation time series ; differential synthetic aperture radar interferometry (DInSAR) ; Distributed databases ; Earth ; Earth surface ; Global positioning systems ; GPS ; Interferometry ; Internet ; Orbits ; Parallel Small BAseline Subset (P-SBAS) ; Products ; Quality assessment ; Quality control ; Radar ; SAR (radar) ; Satellite navigation systems ; Sentinel-1 ; Strain ; Synthetic aperture radar ; Territory ; Time series ; Time series analysis ; Web services</subject><ispartof>IEEE transactions on geoscience and remote sensing, 2019-09, Vol.57 (9), p.6259-6281</ispartof><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2019</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c336t-4dc469446e8f474282b179260193ca183ed06018bb05fada0b5f9a95da40ffa23</citedby><cites>FETCH-LOGICAL-c336t-4dc469446e8f474282b179260193ca183ed06018bb05fada0b5f9a95da40ffa23</cites><orcidid>0000-0002-4950-3122 ; 0000-0002-9584-3347 ; 0000-0002-7245-1551 ; 0000-0002-7296-2749 ; 0000-0001-8555-6494 ; 0000-0002-7843-3565</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/8721519$$EHTML$$P50$$Gieee$$Hfree_for_read</linktohtml><link.rule.ids>314,776,780,792,27901,27902,54733</link.rule.ids></links><search><creatorcontrib>Manunta, Michele</creatorcontrib><creatorcontrib>De Luca, Claudio</creatorcontrib><creatorcontrib>Zinno, Ivana</creatorcontrib><creatorcontrib>Casu, Francesco</creatorcontrib><creatorcontrib>Manzo, Mariarosaria</creatorcontrib><creatorcontrib>Bonano, Manuela</creatorcontrib><creatorcontrib>Fusco, Adele</creatorcontrib><creatorcontrib>Pepe, Antonio</creatorcontrib><creatorcontrib>Onorato, Giovanni</creatorcontrib><creatorcontrib>Berardino, Paolo</creatorcontrib><creatorcontrib>De Martino, Prospero</creatorcontrib><creatorcontrib>Lanari, Riccardo</creatorcontrib><title>The Parallel SBAS Approach for Sentinel-1 Interferometric Wide Swath Deformation Time-Series Generation: Algorithm Description and Products Quality Assessment</title><title>IEEE transactions on geoscience and remote sensing</title><addtitle>TGRS</addtitle><description>We present an advanced differential synthetic aperture radar (SAR) interferometry (DInSAR) processing chain, based on the Parallel Small BAseline Subset (P-SBAS) technique, for the efficient generation of deformation time series from Sentinel-1 (S-1) interferometric wide (IW) swath SAR data sets. We first discuss an effective solution for the generation of high-quality interferograms, which properly accounts for the peculiarities of the terrain observation with progressive scans (TOPS) acquisition mode used to collect S-1 IW SAR data. These data characteristics are also properly accounted within the developed processing chain, taking full advantage from the burst partitioning. Indeed, such data structure represents a key element in the proposed P-SBAS implementation of the S-1 IW processing chain, whose migration into a cloud computing (CC) environment is also envisaged. An extensive experimental analysis, which allows us to assess the quality of the obtained interferometric products, is presented. To do this, we apply the developed S-1 IW P-SBAS processing chain to the overall archive acquired from descending orbits during the March 2015-April 2017 time span over the whole Italian territory, consisting in 2740 S-1 slices. In particular, the quality of the final results is assessed through a large-scale comparison with the GPS measurements relevant to nearly 500 stations. The mean standard deviation value of the differences between the DInSAR and the GPS time series (projected in the radar line of sight) is less than 0.5 cm, thus confirming the effectiveness of the implemented solution. Finally, a discussion about the performance achieved by migrating the developed processing chain within the Amazon Web Services CC environment is addressed, highlighting that a two-year data set relevant to a standard S-1 IW slice can be reliably processed in about 30 h.The presented results demonstrate the capability of the implemented P-SBAS approach to efficiently and effectively process large S-1 IW data sets relevant to extended portions of the earth surface, paving the way to the systematic generation of advanced DInSAR products to monitor ground displacements at a very wide spatial scale.</description><subject>Algorithms</subject><subject>Archives & records</subject><subject>Chains</subject><subject>Cloud computing</subject><subject>Cloud computing (CC)</subject><subject>Data</subject><subject>Data structures</subject><subject>Datasets</subject><subject>Deformation</subject><subject>deformation time series</subject><subject>differential synthetic aperture radar interferometry (DInSAR)</subject><subject>Distributed databases</subject><subject>Earth</subject><subject>Earth surface</subject><subject>Global positioning systems</subject><subject>GPS</subject><subject>Interferometry</subject><subject>Internet</subject><subject>Orbits</subject><subject>Parallel Small BAseline Subset (P-SBAS)</subject><subject>Products</subject><subject>Quality assessment</subject><subject>Quality control</subject><subject>Radar</subject><subject>SAR (radar)</subject><subject>Satellite navigation systems</subject><subject>Sentinel-1</subject><subject>Strain</subject><subject>Synthetic aperture radar</subject><subject>Territory</subject><subject>Time series</subject><subject>Time series analysis</subject><subject>Web services</subject><issn>0196-2892</issn><issn>1558-0644</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><sourceid>ESBDL</sourceid><sourceid>RIE</sourceid><recordid>eNo9kdtOwzAMhiMEEmPwAIibSFx3JGl6CHdlwJg0iUGHuKyy1mGZehhJKrSX4VnJDuLKsv3Zv60foWtKRpQScbeYvOcjRqgYMUG4oOwEDWgUpQGJOT9FA9-JA5YKdo4urF0TQnlEkwH6XawAz6WRdQ01zh-yHGebjelkucKqMziH1ukW6oDiaevAKDBdA87oEn_qCnD-I90KP4JnG-l01-KFbiDIwWiweAItmH35Hmf1V2e0WzWetqXRmz0t2wrPTVf1pbP4rZe1dlucWQvWNl76Ep0pWVu4OsYh-nh-WoxfgtnrZDrOZkEZhrELeFXyWHAeQ6p4wlnKljQRLPZfh6WkaQgV8Um6XJJIyUqSZaSEFFElOVFKsnCIbg97_evfPVhXrLvetF6yYCwNw5Al0Y6iB6o0nbUGVLExupFmW1BS7GwodjYUOxuKow1-5uYwowHgn08TRiN_3B_YTYXZ</recordid><startdate>20190901</startdate><enddate>20190901</enddate><creator>Manunta, Michele</creator><creator>De Luca, Claudio</creator><creator>Zinno, Ivana</creator><creator>Casu, Francesco</creator><creator>Manzo, Mariarosaria</creator><creator>Bonano, Manuela</creator><creator>Fusco, Adele</creator><creator>Pepe, Antonio</creator><creator>Onorato, Giovanni</creator><creator>Berardino, Paolo</creator><creator>De Martino, Prospero</creator><creator>Lanari, Riccardo</creator><general>IEEE</general><general>The Institute of Electrical and Electronics Engineers, Inc. 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We first discuss an effective solution for the generation of high-quality interferograms, which properly accounts for the peculiarities of the terrain observation with progressive scans (TOPS) acquisition mode used to collect S-1 IW SAR data. These data characteristics are also properly accounted within the developed processing chain, taking full advantage from the burst partitioning. Indeed, such data structure represents a key element in the proposed P-SBAS implementation of the S-1 IW processing chain, whose migration into a cloud computing (CC) environment is also envisaged. An extensive experimental analysis, which allows us to assess the quality of the obtained interferometric products, is presented. To do this, we apply the developed S-1 IW P-SBAS processing chain to the overall archive acquired from descending orbits during the March 2015-April 2017 time span over the whole Italian territory, consisting in 2740 S-1 slices. In particular, the quality of the final results is assessed through a large-scale comparison with the GPS measurements relevant to nearly 500 stations. The mean standard deviation value of the differences between the DInSAR and the GPS time series (projected in the radar line of sight) is less than 0.5 cm, thus confirming the effectiveness of the implemented solution. Finally, a discussion about the performance achieved by migrating the developed processing chain within the Amazon Web Services CC environment is addressed, highlighting that a two-year data set relevant to a standard S-1 IW slice can be reliably processed in about 30 h.The presented results demonstrate the capability of the implemented P-SBAS approach to efficiently and effectively process large S-1 IW data sets relevant to extended portions of the earth surface, paving the way to the systematic generation of advanced DInSAR products to monitor ground displacements at a very wide spatial scale.</abstract><cop>New York</cop><pub>IEEE</pub><doi>10.1109/TGRS.2019.2904912</doi><tpages>23</tpages><orcidid>https://orcid.org/0000-0002-4950-3122</orcidid><orcidid>https://orcid.org/0000-0002-9584-3347</orcidid><orcidid>https://orcid.org/0000-0002-7245-1551</orcidid><orcidid>https://orcid.org/0000-0002-7296-2749</orcidid><orcidid>https://orcid.org/0000-0001-8555-6494</orcidid><orcidid>https://orcid.org/0000-0002-7843-3565</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Algorithms Archives & records Chains Cloud computing Cloud computing (CC) Data Data structures Datasets Deformation deformation time series differential synthetic aperture radar interferometry (DInSAR) Distributed databases Earth Earth surface Global positioning systems GPS Interferometry Internet Orbits Parallel Small BAseline Subset (P-SBAS) Products Quality assessment Quality control Radar SAR (radar) Satellite navigation systems Sentinel-1 Strain Synthetic aperture radar Territory Time series Time series analysis Web services |
title | The Parallel SBAS Approach for Sentinel-1 Interferometric Wide Swath Deformation Time-Series Generation: Algorithm Description and Products Quality Assessment |
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