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.

This paper addresses the problem of interferometric noise reduction in Synthetic Aperture Radar (SAR) interferometry based on sparse and redundant representations over a trained dictionary. The idea is to use a Proximity-based K-SVD (ProK-SVD) algorithm on interferometric data for obtaining a suitable dictionary, in order to extract the phase image content effectively. We implemented this strategy on both simulated as well as real interferometric data for the validation of our approach. For synthetic data, three different training dictionaries have been compared, namely, a dictionary extracted from the data, a dictionary obtained by a uniform random distribution in $[-\pi ,\pi ]$ , and a dictionary built from discrete cosine transform. Further, a similar strategy plan has been applied to real interferograms. We used interferometric data of various SAR sensors, including low resolution C-band ERS/ENVISAT, medium L-band ALOS, and high resolution X-band COSMO-SkyMed, all over an area of Mt. Etna, Italy. Both on simulated and real interferometric phase images, the proposed approach shows significant noise reduction within the fringe pattern, without any considerable loss of useful information.

This article presents an innovative, parallel implementation of the Small BAseline Subset (SBAS) approach to automatically and efficiently process large volumes of multitemporal differential synthetic aperture radar (SAR) interferometry (DInSAR) interferograms generated at the native full spatial resolution of the SAR images. The starting point of the developed full-resolution parallel-SBAS (FR P-SBAS) technique involves some algorithmic extensions for improving the quality of the DInSAR time series to effectively analyze extended deformations and localized displacement phenomena, such as those affecting single buildings and infrastructures. The main focus of the work is on the efficient and scalable FR P-SBAS processing chain implementation, extensively exploiting graphical processing unit (GPU) architectures. Moreover, the presented scalability analysis demonstrates the GPU capability of efficiently generating full-resolution displacement time series starting from large DInSAR datasets. Furthermore, it is also shown that the implemented processing solution easily allows us to deal with SAR data acquired through the Stripmap and TOPS modes. To assess the quality of the generated DInSAR products, an extensive experimental analysis is also shown, based on long sequences of X-Band COSMO-SkyMed Stripmap and C-Band Sentinel-1 TOPS acquisitions relevant to the Campi Flegrei Caldera (Southern Italy), which is monitored through a dense GNSS network. The presented results demonstrate the effectiveness of the FR P-SBAS processing chain in retrieving multifrequency and multiplatform displacement time series at the full spatial resolution with subcentimetric accuracy and in very short time frames, from a few hours for the COSMO-SkyMed datasets up to some tens of hours for the Sentinel-1 case.

Free-space antenna radiation pattern estimation from intensity measurements made in reverberating environments has been investigated by several authors. We extend our previous analysis of a simple directivity estimator appropriate to the case where the antenna under test (and the measuring probe) is located in an ideal reverberation chamber, where the reverberation-field components are Gaussian distributed and uncorrelated to a more realistic reverberation chamber model, where the field statistics deviate from the ideal ones.

Sentinel-1 is the first of a family of satellites designed to provide a data stream for the European environmental monitoring program known as Copernicus. Sentinel-1 constellation has been specifically designed to perform, over land, advanced Differential Interferometric Synthetic Aperture Radar (DInSAR) analyses for the investigation of Earth's surface displacements. In particular, owning to its 6-day revisit time and its innovative acquisition mode, which is referred to as Terrain Observation by Progressive Scans (TOPS) and is fundamental for guaranteeing a global spatial coverage, Sentinel-1 constellation is contributing to the creation of a framework for the exploitation of “Big Data” for Earth Observation (EO) applications.In this paper, we present an efficient and automatic implementation of the Parallel Small BAseline Subset (P-SBAS) DInSAR algorithm, specifically intended for the processing of Sentinel-1 SAR data. The algorithm is able to run on distributed computing infrastructures by effectively exploiting a large number of resources, and allows the generation of ground displacement time-series. The aim of this paper is to show that it is possible to automatically and continuously process, in a short time frame, very large sequences of Sentinel-1 data, thus allowing us to perform advanced interferometric analyses at an unprecedented large scale. In addition, the proposed Sentinel-1 P-SBAS algorithm has also been tested on commercial public cloud computing platforms, such as those provided by the Amazon Web Services.The presented Sentinel-1 P-SBAS processing chain is well suited to build up operational services for the easy and rapid generation of advanced interferometric products, which can be very useful not only for scientific purposes but also for the risk management and the natural hazard monitoring

This work is aimed at describing the development of an efficient interferometric processing chain, based on the well-known advanced Differential Interferometric Synthetic Aperture Radar (DInSAR) algorithm referred to as Small BAseline Subset (SBAS) technique, for the generation of Sentinel-1A (S1-A) Interferometric Wide Swath (IWS) deformation time-series. Due to the TOPS mode characterizing the IWS acquisitions, the existing SBAS processing chains was properly adapted with new procedures for efficiently handling the S1-A data. The developed SBAS-DInSAR chain has been tested on both S1-A and TOPS RadarSAT-2 interferometric dataset, clearly demonstrating the capability of the developed SBAS-DInSAR processing chain to effectively investigate land subsidence phenomena affecting large areas.