The IRIDE constellation is an ambitious Italian space program that will support the national authorities in their analyses and monitoring activities, with a focus on Italian territory mapping. It will comprise a series of small satellite subconstellations exploiting a wide range of remote sensing technologies. This article analyses the NIMBUS X-Band synthetic aperture radar (SAR) IRIDE subconstellation, exploring potential orbital configurations beyond the more conventional and widespread dawn-dusk sun-synchronous orbit (SSO) one. In particular, starting from the mission target, we show that a 49° mid-inclination orbit (MIO) in a right-looking StripMap acquisition mode represents a highly effective choice for NIMBUS. We demonstrate that this configuration enhances the systematic coverage of the Italian territory with six nodal days of interferometric revisit time and high spatial resolution, thereby facilitating detailed observations of both natural phenomena and anthropic activities. In terms of differential SAR interferometry (DInSAR) performance, we prove that MIOs do not show significant limitations for what attains the critical baseline and geometric distortions. In addition, MIOs may lead to future advances in creating 3-D displacement maps because they allow for the recovery of the North-South deformation component that, conversely, cannot be precisely measured with DInSAR systems operating in SSO.

The Campi Flegrei caldera (southern Italy) is an active volcano whose last eruption occurred in 1538. This caldera is characterized by ground displacements (subsidence or uplift), typically identified with the term "bradyseism". Over the last two decades the Campi Flegrei caldera was affected by a phase of progressively increasing uplift and it was accompanied by increasing seismicity and geochemical anomalies. The ground displacement pattern is monitored through GNSS and Differential SAR Interferometry (DInSAR) analysis, the latter focused on the Sentinel-1 SAR data exploitation. According to the available measurements, the displacement pattern is mainly radial with a maximum uplift in the area corresponding to Rione Terra (Pozzuoli). However, we have also detected a geodetic anomaly, near the Mt. Olibano–Accademia area, where most of the recent seismicity of the caldera is concentrated, which become clearly recognizable starting from 2021 and further developed during 2022 and 2023.

In this work, we investigate potential orbital configurations of NIMBUS, one of the Synthetic Aperture Radar (SAR) components of the Italian constellation IRIDE, which will be completed by 2026 under the management of the European Space Agency (ESA), with the support of the Italian Space Agency (ASI). IRIDE is conceptualized as a "constellation of constellations", comprising multiple sub-constellations of satellites positioned in Low Earth Orbit (LEO) and equipped with various sensing technologies, including SAR and optical sensors with medium- and high-resolution capability, operating at different frequency ranges. Given both institutional and commercial users the constellation is expected to fulfill, this work introduces a methodology to compare different orbital configurations and finally identify the optimal orbital arrangement that maximizes the Italian national coverage within the shortest revisit time possible, considering both the capabilities and imaging performance of a SAR system planned for deployment in orbit.

This work describes the airborne Synthetic Aperture Radar (SAR) infrastructure developed at the Institute for Electromagnetic Sensing of the Environment (IREA) - National Research Council of Italy (CNR), Naples, Italy.The current infrastructure consists of a flight and a ground segment, including: a multi-frequency airborne SAR sensor operating at X- and L-band, owned by IREA-CNR; an IT platform for data storage and processing, located at the laboratories of IREA-CNR; an airborne SAR data processing chain, completely developed at IREA-CNR and University Parthenope, Naples, Italy.To show the current capabilities of this infrastructure in terms of characteristics of the final products as well as of quick response in emergency cases, a real world application scenario, namely, the monitoring of the volcanic area of Stromboli, Italy, in the frame of an agreement between IREA-CNR and the Department of Civil Protection of the Italian Presidency of the Council of Ministers, is presented.The short-term future perspectives related to the exploitation of this airborne SAR infrastructure are finally drawn, according to ongoing strengthening actions supported by already assigned fundings.

We present in this study an enhancement to our previous work, in which an automated method, based on Convolutional Neural Networks (CNNs), has been developed for identifying co-seismic ground deformation patterns within the Differential SAR Interferometry (DInSAR) maps generated through the EPOSAR service of the European Plate Observing System (EPOS) Research Infrastructure. The implemented improvements have been achieved through two main lines of actions. Firstly, the generation of the synthetic dataset, used to train the developed CNN, has been enhanced; this concerns the improvement of the simulation of possible atmospheric disturbance within the DInSAR interferograms, and the introduction of a simulator of phase unwrapping errors. Secondly, the original CNN-based deformation pattern detector layout, performing a binary classification, has been modified to account for a multiclass deformation pattern classification. This allows us extending the capability of the system which, in addition to detect co-seismic deformation patterns, may also provide information on the earthquake source characteristics. The presented solution will be included in the EPOSAR DInSAR processing chain.

Phase unwrapping (PhU) is a challenging step in multi-temporal (MT) Differential SAR Interferometry (DInSAR) techniques that may have a significant impact on the accuracy of the retrieved displacements. In this paper, we present a simple solution to enhance a previously developed Compressive Sensing (CS)-based PhU approach, allowing us to improve its performance in particularly challenging surface displacement scenarios. The proposed method, without altering the technical framework of the CS-based original solution, specifically targets the refinement of the MT interferometric sequence selection process. In particular, starting from the pivotal role of the Delaunay triangulation in the SAR data acquisitions temporal/perpendicular baseline plane to identify the bulk multi-temporal DInSAR interferograms sequence, we easily enrich this sequence by properly adding highly coherent interferograms which have not been initially selected. The overall study is focused on multi-look L-band interferograms of the SAOCOM-1 constellation, relevant to the Stromboli Island (Italy), and a comparative analysis between the results obtained by applying the enhanced method and its original version is presented, showing the improved capability of the former.

In this work we focus on the estimation of the soil permittivity from L-band polarimetric Synthetic Aperture Radar (SAR) data. To this aim, we consider SAOCOM-1 data collected in December 2021, over the province of La Pampa, in Argentina. The soil permittivity estimates, obtained through the Polarimetric Two-Scale Model (PTSM), are compared with the in-situ measurements collected at the same time of the SAR acquisition. Our results of the dielectric constant retrieved for the full scene are in accordance with a semi-arid landscape where the mode of the retrieved estimates is around εr~4 and a 2.41% relative error was found for ground data.

This work is focused on the forthcoming ROSE-L mission, developed by the European Space Agency (ESA) as part of the Copernicus Expansion Programme. Among the different advanced engineering solutions that will be exploited by the system, we concentrate on the ScanSAR acquisition mode and show how to enable ROSE-L for a high gain two-look configuration. More specifically, we propose a solution aimed at achieving different goals.The first goal is focused on better tailoring the ROSE-L system, originally designed for a one-look ScanSAR mode configuration, to a more attractive two-look one. The second goal is to retain the azimuth resolution and the range swath of the original system design. The third goal is to be compliant with the main system specifications, with respect to the one- look mode configuration.To achieve these goals, we propose to properly shape the radiated azimuth beam, without upsetting the original design of the ROSE-L radar antenna and taking advantage of the degrees of freedom offered by its current layout.

The problem of the identification of Radio Frequency Interferences (RFI) that corrupt Synthetic Aperture Radar (SAR) data is addressed in this work.In particular, we propose a new approach based on the minimization of the Bayesian Information Criterion (BIC) able to detect, on a pixel-by-pixel basis, the RFI signals within the considered SAR data.The performance of the proposed approach is assessed by means of both simulated and real-world airborne L-band SAR data corrupted by RFI signals.

The Extended Timing Annotation Dataset (ETAD) product consists of a set of correction layers to improve the range and azimuth timing of Sentinel-1 (S1) Synthetic Aperture Radar (SAR) images. Moreover, the ETAD layers also allow the mitigation of the Atmospheric Phase Screen (APS) component which may affect the Interferometric SAR products. In this paper, we present a detailed experimental analysis to investigate the effectiveness of the S1 ETAD correction layers in removing the APS component from Differential Synthetic Aperture Radar (DInSAR) products (interferograms and deformation time series).In particular, the performance analysis of the ETAD APS correction has been carried out by exploiting the Parallel Small Baseline Subset (P-SBAS) approach to process a large dataset of 104 S1 images acquired along ascending orbits during the 2018-2020 time span over Central/Southern Italy. Several statistical metrics have been then applied both to the 278 generated interferograms and to the P-SBAS deformation time series, produced at medium spatial resolution (about 40 m), to quantitatively investigate the validity of the ETAD APS correction.

This work shows that additional ScanSAR capabilities, with respect to those achievable through the current synthetic aperture radar (SAR) system of the forthcoming Radar Observation System for Europe at L-band (ROSE-L) mission, may be easily enabled through a proper shaping of the azimuth pattern of the TX radar antenna. In particular, we show that by properly acting only on the distribution of the input excitations of the elements of the currently designed TX antenna array, we can move from a single azimuth-look ScanSAR configuration to a more attractive two-look one. This result is achieved without upsetting the current system architecture and without changing the current mission parameters [such as azimuth resolution, range swath, and pulse repetition frequency (PRF)], all tailored to a one-look ScanSAR configuration. On the other hand, the proposed two-look mode requires to double the azimuth beamwidth with respect to the one-look mode, thus leading to an unavoidable antenna gain decrease, whose amount is approximately 3 dB. The presented analysis also shows that, by still acting on the distribution of the input excitations of the TX antenna array, the scalloping effect can be significantly mitigated with respect to that of the current system design, in both scenarios relevant to the original one-look ScanSAR configuration and the proposed two-look one.

We present in this work the results of an extensive, full resolution Parallel SBAS (P-SBAS) processing activity carried out on large multi-temporal SAR data archives of COSMO-SkyMed first (CSK) and second (CSG) generation SLC images, relevant to some of the main cities of the Italian territory, acquired from ascending and descending orbits since 2009. In particular, we first summarize the key points of the exploited, full resolution P-SBAS processing chain. Subsequently, we also describe the main algorithmic developments aimed to exploit large temporal sequences of CSK and CSG SLC data relevant to wide areas. Finally, we present several results highlighting, through some selected examples, that the use of high resolution SAR images, such as those of the CSK-CSG constellation, processed with techniques like the full resolution P-SBAS technique approach, may provide valued-added information that, properly integrated with in-situ measurements, can be very relevant for the assessment of the built-up environment health conditions, at the national scale.

The retrieval of the topographic changes induced by the eruptive activity of the Stromboli volcano, located in the Tyrrhenian Sea off Sicily (Italy), is the main topic of this contribution. In particular, we focus on the Stromboli activity during the July 2019 - October 2022 time period that was characterized by two violent explosions and months of lava emission inducing topography changes. These variations have been measured through the comparison of the generated airborne single-pass interferometric Synthetic Aperture Radar Digital Elevation Models.

Over the past 30 years it has been widely demonstrated the effectiveness of the Differential Synthetic Aperture Radar Interferometry (DInSAR) technique to retrieve surface deformation information relevant to tectonically active areas. However, this technique exhibits some limitations due the presence of possible decorrelation effects, phase unwrapping errors, and artefacts due to the temporal/spatial variability of the atmospheric conditions between the SAR acquisition pairs exploited to generate the interferograms and the corresponding deformation maps or time series. A challenging situation may arise when an earthquake event occurs and a co-seismic DInSAR deformation map is generated to quickly support risk management operations. In this scenario, having an automatic process reducing the uncertainties of the retrieved, DInSAR-based, surface deformation information could highly improve the quality of the products made available to the scientific community and of the service provided to the national disaster recovery authorities. We present in this work a solution, based on standard CNN architectures embedded in the DInSAR processing chain of the EPOSAR service, developed within the European Plate Observing System (EPOS) Research Infrastructure, to automatically identify co-seismic ground deformation patterns.

We present in this work some advances of the Differential SAR Interferometry (DInSAR) technique referred to as Parallel Small BAseline Subset (P-SBAS) approach, allowing us to effectively process the recently available L-band SAR images acquired by the Argentinian SAOCOM-1 constellation. This activity has been carried out within the DInSAR-3M project, co-funded by the Italian Space Agency, which is aimed to develop technical solutions for the joint exploitation of multi-frequency (X-, C- and L- band) and multi-platform (spaceborne and airborne) DInSAR measurements. The main goal is the generation of surface deformation time series and corresponding mean displacement velocity maps, spatially and temporally dense, for the multi-scale analysis of natural and anthropogenic phenomena.The assessment of the developed advances has been carried out by processing several SAOCOM-1 SAR datasets acquired over Italy between 2019 and 2022.

In this paper, we present an innovative Phase Unwrapping (PhU) procedure for Multi Temporal DInSAR techniques, based on the Extended Minimum Cost Flow technique schema. The developed algorithm benefits from the Compressive Sensing (CS) theory. In particular, the procedure consists of the cascade of three steps. At first, the set of spatially connected arcs between close pixels is identified by selecting the points to be unwrapped and linking them through the Delaunay algorithm; in the second step, the temporal PhU of all the arcs is carried out by using the CS approach and an L1-norm estimator. In the last step, the algorithm uses the unwrapped arcs to perform spatial PhU of each interferogram using MCF technique. To assess the performance of the developed approach, we analyze the area related to Stromboli volcano (Italy) by processing the dataset acquired by Sentinel-1 constellation over descending orbit from 2016 to 2021.

We investigate in this work the soil moisture retrieval from P-band polarimetric Synthetic Aperture Radar (SAR) data. In particular, the analyzed SAR data were collected during a helicopter-borne campaign conducted over the Apulian Tavoliere plain, Southern Italy, on July 2021. The soil moisture estimates, obtained through the Polarimetric Two-Scale Model (PTSM), are then synthetically compared with in-situ measurements collected at the same time of the radar survey.

It is widely recognized that SAR images can suffer from the presence of RFI, especially at the low frequencies of the microwave spectrum. Such nuisance signals can significantly impair the quality of the SAR data with unwanted effects on the final products. This paper describes a processing strategy for the detection and cancellation of RFI signals corrupting airborne SAR raw-data. In addition, the performance of the proposed strategy is assessed by exploiting airborne L-band FMCW-SAR data.

We present in this work a solution to identify Phase Unwrapping (PhU) errors in redundant sequences of small baseline Differential SAR Inteferometry (DInSAR) interferograms like those exploited by the Small Baseline Subset (SBAS) approach, which allows the retrieval of displacement time series. In particular, the temporal information is exploited through the temporal coherence parameter, which represents a point-like quality indicator of the PhU solutions within advanced DInSAR methods like the above mentioned SBAS technique. First of all we show that this parameter needs to be carefully considered because it may lose sensitivity when the number of images of the exploited dataset increases. Subsequently, we explore a different use of the temporal coherence, moving from a single valued parameter to a time-dependent function. This permits to regain sensitivity and to reduce the number of interferograms eligible to be corrected. The last part of the work is devoted to present a case study where the proposed solution allows the identification of PhU errors.

The Extended Timing Annotation Dataset (ETAD) product provides azimuth and range timing corrections to improve the geometric accuracy of Sentinel-1 (S-1) Synthetic Aperture Radar (SAR) images. Moreover, the product also contains dedicated layers for mitigating atmospheric path delays, estimated by using Numerical Weather Prediction (NWP) models. In this paper, we present an analysis on the exploitation of the ETAD correction layers for the removal of the Atmospheric Phase Screen (APS) signals from Differential Synthetic Aperture Radar (DInSAR) interferograms generated at medium/high spatial resolution. The performed analysis, involving a dataset acquired over the Napoli bay area, highlighted that the computed ETAD APS corrections introduces several artefacts in the corrected interferograms. In particular, these artefacts are more evident where a relevant topography gradient is present, because they are caused by the ETAD DEM height variations due to the different projections within the specific range-azimuth grid of each S-1 burst image. A simple solution to remove the above mentioned artefacts is also presented, together with some results confirming the validity of this method.

Airborne synthetic aperture radar (SAR) data focusing in presence of highly variable squint angles is addressed in this work. To do this, a time-domain SAR focusing algorithm has been developed to account for the target-dependent nature of the acquisition squint angle. For comparison purposes, we have considered also a SAR processor based on the use of an azimuth-invariant processing squint angle. Moreover, real data acquired by two different X-band airborne SAR systems have been analyzed. The aims of the work are twofold. First, we show that the adoption of an azimuth-invariant processing squint angle, typically pursued by computationally efficient focusing strategies, may become inappropriate in several airborne missions, which are typically corrupted by motion errors induced by atmospheric turbulence. On the contrary, these problems are circumvented through the developed time-domain focusing strategy, which exploits a target-dependent processing squint angle that is coincident with the acquisition one. Second, we show that the proposed focusing strategy must be properly revised in the case of interferometric processing. In particular, we show that if the (target-dependent) acquisition squint angle is different for the two interferometric channels used, we have to move toward a different focusing paradigm, say, an interferometric focusing solution. This is again based on considering a target-dependent processing squint angle, but for each target, this processing angle should be equal neither to the acquisition squint angle of the “master” acquisition nor to that of the “slave” one: it must instead lie halfway between them. We prove that this approach is appropriate to obtain high interferometric coherence, and it is sub-optimal to separately focus the interferometric channels.

In this work, we present an advanced implementation of the full resolution Parallel Small BAseline Subset (P-SBAS) DInSAR processing chain, aimed to effectively and automatically generate DInSAR products (displacement time series and corresponding velocity maps) related to single buildings and infrastructures over the whole Italian territory. The proposed full resolution P-SBAS pipeline exploits innovative hardware and software parallel technologies based on GPUs, which are able to efficiently process large amounts of full resolution DInSAR data stacks in reasonable time frames and with high scalability. The presented results, achieved by processing very large archives of full resolution X-band first and second generation COSMO-SkyMed data and C-band Sentinel-1 images, demonstrate the effectiveness of the proposed solution in terms of computing time and computational efficiency.

We present in this work the first results of the project referred to as DInSAR-3M, which is aimed to generate surface deformation time series and mean velocity maps, spatially and temporally dense, for the multi-scale analysis of natural and anthropogenic phenomena. The project objective will be pursued through the use of the advanced Differential SAR Interferometry (DInSAR) technique referred to as Parallel Small BAseline Subset (P-SBAS) approach and the development of a solution for the integration of multi-frequency (X-, C- and L- band) and multi-platform DInSAR measurements. In the following, we present the first results focused on the interferometric exploitation of the recently available L-band SAR images acquired by the Argentinian SAOCOM constellation.

In recent years, airborne Synthetic Aperture Radar (SAR) remote sensing is becoming more and more attractive, thanks to the operational flexibility and observation capabilities provided by aerial SAR systems, and also due to the comparatively low-cost of these radar devices. In this work, we show the preliminary results relevant to the first acquisition campaign of the new Italian airborne Multiband Interferometric and Polarimetric SAR (MIPS) system, which is based on the Frequency Modulated Continuous Wave (FMCW) technology, and is able to operate at both X- and L-band.

In the future ROSE-L mission, supported by the European Space Agency (ESA) as part of the Copernicus Expansion Programme, different advanced acquisition techniques will be simultaneously experienced, namely, ScanSAR, Scan On Receive and Digital Beam Forming on receive. This work shows that additional ScanSAR capabilities with respect to those achievable through the current ROSE-L system design may be enabled through proper shaping of the azimuth pattern of the TX radar antenna. In particular, we show that without upsetting the current system architecture, and properly acting only on the distribution of the input excitations of the elements of the currently designed TX array antenna, we can obtain a significant mitigation of the scalloping effect and/or move from a single azimuth look ScanSAR configuration to a more attractive two-look one.

We present in this paper a comparative analysis focused on the estimation and removal of the Atmospheric Phase Screen (APS) from DInSAR deformation measurements relevant to volcanic areas characterized by significant displacements and topography. These are scenarios where it is often difficult to separate the interferometric phase component due to atmospheric artifacts from the one related to the actual ground deformation. In particular, we investigate the APS correction performance achieved by applying two different approaches: i) the first relying on the exploitation of the external meteorological ERA-5 data, ii) the second based on filtering out atmospheric contributions from the DInSAR time series by exploiting their statistical properties both in space and in time. For the experimental analysis we analyze very large Sentinel-1 datasets acquired over two volcanic areas particularly challenging from the point of view of the APS estimation: La Palma island (Canary, Spain), focusing on the last eruption occurred on 19 September 2021, and Mt. Etna (Sicily, Italy).

In a recent publication, Ansariet al.(2021) claimed (see, in particular, the Discussion and Recommendation Section in their article) that the advanced differential SAR interferometry (InSAR) algorithms for surface deformation retrieval, based on the small baseline approach, are affected by systematic biases in the generated InSAR products. Therefore, to avoid such biases, they recommended a strategy primarily focused on excluding “the short temporal baseline interferograms and using long baselines to decrease the overall phase errors.” In particular, among various techniques, Ansariet al.(2021) identified the solution presented by Manuntaet al.(2019) as a small baseline advanced InSAR processing approach where the presence of the above-mentioned biases (referred to as a fading signal) compromises the accuracy of the retrieved InSAR deformation products. We show that the claim of Ansariet al.(2021) is not correct (at least) for what concerns the mentioned approach discussed by Manuntaet al.(2019). In particular, by processing the Sentinel-1 dataset relevant to the same area in Sicily (southern Italy) investigated by Ansariet al.(2021), we demonstrate that the generated InSAR products do not show any significant bias.

In this work, we present the ground deformation analysis of the Italian Peninsula carried out via the Sentinel-1 (S-1) Parallel Small BAseline Subset (P-SBAS) approach. In particular, we generate surface deformation time-series and the corresponding mean deformation velocity maps by considering the overall archive of S-1 data acquired from both ascending and descending orbits during the March 2015 - December 2018 period. Moreover, we perform a large-scale comparison of the retrieved results with the deformation measurements of the available GPS stations deployed on the Italian territory and provided by the Nevada Geodetic Laboratory at the University of Nevada, Reno, USA (UNR-NGL). Finally, we properly combine the ascending and descending deformation time-series in order to compute the vertical and east-west components of the retrieved surface displacements. The achieved results demonstrate the capability of the P-SBAS processing chain to efficiently process large S-1 datasets to investigate ground deformation dynamics of large portions of the Earth surface.

We present the implementation of a global archive of Differential Synthetic Aperture Radar Interferometry (DInSAR) co-seismic deformation maps. The archive has been generated by automatically processing all the Copernicus Sentinel-1 data spanning about 300 significant (at least Mw > 5.5) earthquakes all over the Earth. An empirical relation between magnitude and epicenter depth was considered to limit the study to those earthquakes that can likely induce ground deformation. DInSAR processing has been carried out within a Cloud-Computing (CC) environment, specifically the Amazon Web Services, to benefit of high processing capacity. The generated DInSAR results are then made freely and openly available to the Solid Earth scientific community through the European Planet Observing System (EPOS) Research Infrastructure.

We present an automatic pipeline implemented within the Amazon Web Services (AWS) Cloud Computing platform for the interferometric processing of large Sentinel-1 (S1) multi-temporal SAR datasets, aimed at analyzing Earth surface deformation phenomena at wide spatial scale. The developed processing chain is based on the advanced DInSAR approach referred to as Small BAseline Subset (SBAS) technique, which allows producing, with centimeter to millimeter accuracy, surface deformation time series and the corresponding mean velocity maps from a temporal sequence of SAR images. The implemented solution addresses the aspects relevant to i) S1 input data archiving; ii) interferometric processing of S1 data sequences, performed in parallel on the AWS computing nodes through both multi-node and multi-core programming techniques; iii) storage of the generated interferometric products. The experimental results are focused on a national scale DInSAR analysis performed over the whole Italian territory by processing 18 S1 slices acquired from descending orbits between March 2015 and April 2017, corresponding to 2612 S1 acquisitions. Our analysis clearly shows that an effective integration of advanced remote sensing methods and new ICT technologies can successfully contribute to deeply investigate the Earth System processes and to address new challenges within the Big Data EO scenario.

In this work, a time-domain procedure is presented to focus airborne Synthetic Aperture Radar (SAR) data collected in the presence of strong variations of the squint angle during the radar acquisition. The approach is tested on two datasets acquired by two different X-band airborne SAR systems.

We present an unsupervised and automatic system for volcano deformation monitoring via the Copernicus Sentinel-1 data. The system relies on the Parallel Small BAseline Subset (P-SBAS) approach, permitting us to generate updated displacement time series at every new Sentinel-1 acquisition over a selected area of interest in a fast and accurate way. The service is currently operative to monitor the main active Italian volcanoes in the framework of cooperation with the Italian Department of Civil Protection. The system is potentially extendable to every area on the Earth, thus making it suitable for surface displacement monitoring of a large variety of phenomena. Finally, the obtained results are made available to the scientific community through the EPOS Research Infrastructure.

In this work, we present some results of the flight-test campaign carried out with the AXIS system, which is an Italian single-pass interferometric airborne X-Band Synthetic Aperture Radar (SAR) based on the frequency modulated continuous wave technology. The system has recently been developed in the frame of a cooperation between a public research institute (IREA-CNR) and a private company (Elettra Microwave). The campaign was performed in 2018 over the Salerno area, South of Italy, and some corner reflectors (CRs) were deployed over the illuminated area with the aim of verifying the system performances in terms of geometric resolution and target location.

In this work we present a cloud-computing based strategy for generating surface displacement time series and mean deformation velocity maps of very large areas by exploiting Sentinel-1 (S1) data. Our approach relies on the simultaneous exploitation of a fast access to the S1 data archives, High Performance Computing resources and external geodetic data.The presented pipeline is based on an advanced cloud-computing implementation of the differential SAR interferometry (DInSAR) Parallel Small BAseline Subset (P-SBAS) processing chain, which allows the fully unsupervised processing of huge Interferometric Wide Swath (IWS) Sentinel-1 data volumes.In particular, for what concerns the S1 data archives, we benefited from the NASA's Alaska Satellite Facility (ASF) Distributed Active Archive Center (DAAC), which stores and distributes S1 SAR data from Amazon Web Service (AWS) for advancing Earth science research. The ASF-DAAC allowed us to reach a very high performance level in terms of downloading time and system reliability. Moreover, we exploited the geodetic measurements provided from the Magnet + Global GPS Network Map of the Nevada Geodetic Laboratory at the University of Nevada, Reno, USA (UNR-NGL), which supply GPS measurements daily updated and continuosly available. The GPS measurements were used to account for regional trends and to better discriminate the low (tectonic) and high (local) frequency deformation patterns.The presented solution is highly scalable and has been migrated to the Amazon Web Service (AWS) environment. It runs out along an automatic routine to generate the mean deformation velocity maps of the vertical and horizontal (East-West) displacement components of the whole investigated area.The developed pipeline has been tested on ascending and descending Sentinel-1 archives acquired over a large area of Southern California (US), which extends over about 150,000 square kilometers.

The Differential Synthetic Aperture Radar Interferometry (DInSAR) remote sensing technique permits to investigate the temporal behaviour of the detected displacements through the generation of the deformation time-series. In this scenario the Phase Unwrapping (PhU) is a crucial point where several errors could occur since the problem is intrinsically ill-posed. In this paper we present a technique to correct unavoidable PhU errors and limit their impact in the final deformation time-series. The proposed approach works pixel-by-pixel and is based on the combination of an L1-norm inversion for the identification of the possible PhU errors and a genetic algorithm (GA) for the search of the best fitting solution. We include the technique in the Small Baseline Subset (SBAS) DInSAR processing chain to correct the results of the Extended Minimum Cost Flow algorithm, but in principle it can be used as a correction step after a generic PhU procedure used in SBAS. Results from MonteCarlo simulations are shown in this paper while real data cases will be presented at the conference.

We present an unsupervised tool for the automatic generation of co-seismic displacement maps by using satellite Differential Synthetic Aperture Radar Interferometry (DInSAR) technique. The tool relies on the use of Sentinel-1 SLC Interferometric Wide Swath (IWS) data and main earthquake information, the latter retrieved from on-line global catalogs. In particular, the tool collects location, depth and magnitude of a seismic event and immediately triggers the data download and the interferometric process over the area affected by the earthquake. The generated DInSAR results (mainly interferograms and displacement maps) are then made available to the Solid Earth scientific community through the EPOS Research Infrastructure. Moreover, the implemented system is also used to serve the Italian Department of Civil Protection in case of main seismic events in Italy. Finally, due to the Sentinel-1 characteristics, the obtained results can contribute to the generation of a global database of DInSAR co-seismic displacement maps.

The retrieval of soil moisture and surface roughness from P-Band Synthetic Aperture Radar (SAR) data is here addressed. In particular, the presented methodology is based on the Polarimetric Two Scale Model (PTSM) recently proposed in the literature, and exploits the combination of SAR data collected along two antiparallel flight-tracks flown on the same area with a very short revisit time. Some considerations about the effects of the SAR acquisition geometry on the retrieval results are also provided. The used dataset is relevant to a desert campaign carried out in 2018 with the helicopter-borne P-band SAR system owned by the Italian Space Agency.

The Italian Space Agency (ASI) has recently entrusted CO.RI.S.T.A. with the development of a radar system that can be mounted onboard small airplanes or helicopters and may operate, at different frequencies belonging to the P-Band, either as Synthetic Aperture Radar (SAR) or as Sounder. In this work, we present preliminary results of the helicopter-borne desert campaign carried out with this system in 2018 over the Erfoud area, Morocco, in the frame of a project that has involved different public Italian Research Institutes and Universities.

We investigate the deformation of Mt. Etna volcano (Italy) by exploiting the DInSAR technique referred to as the Small BAseline Subset (SBAS) algorithm. In particular, we take advantage of the multi-sensor data processing capability of the SBAS algorithm which allows us to generate Mt. Etna mean deformation velocity maps and the corresponding time series in the last twenty-five years. To achieve this task, we exploit different set of SAR data collected by ERS-1/2, ENVISAT, COSMO-SkyMed and Sentinel-1 radar sensors in the 1992-2018 time interval. We, also, show the deformation pattern induced by the recent seismo-volcanic crisis occurring from 24 to 27 December 2018. The main results show that the East-West displacement map highlights the most significant entities, whose maximum values exceed 30 cm towards the West and 40 towards the East on the summit of the volcano. In this scenario, the measurements permit to model the geometry and characteristics of the causative deformation sources; preliminary results suggest the presence of two different sources: a shallow dyke feeding the eruptive activity at summit craters and a major strike-slip fault/s on the southeastern flank nucleating the 26thmainshock.

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 shows the design parameters and specifications of the AXIS system, which is an Italian single-pass interferometric airborne X-Band Synthetic Aperture Radar (SAR) based on the frequency modulated continuous wave technology. The activity is framed within a cooperation agreement signed between Elettra Microwave S.r.l and the Institute for Electromagnetic Sensing of the Environment (IREA) of the Italian National Research Council (CNR).

An innovative method for radiometric calibration of SAR images affected by induced-topography distortions is proposed. The analytical formulation underlying the proposed method is rigorously obtained by using the fundamental concepts of the differential geometry of the surface. It encompasses a suitable 3D geometric description based on a trajectory-centric cylindrical coordinate system, a convenient parametrization of the illuminated terrain surface, and an inverse mapping approach. The practical effectiveness of the GICAL method is successfully demonstrated by using COSMO-SkyMed SAR data.

A novel look-angle function based formulation of the topography-induced backscattering matrix rotation is proposed in this paper. The proposed formulation is obtained by relying on a suitable cylindrical coordinate system and a suitable parametric representation of the illuminated surface. A concise functional form for the polarization orientation angle shifts, whose expression is given uniquely in terms of the partial derivative of the look-angle function along the azimuthal (or along track) direction, is derived. As a result, a unifying analytical approach for the calibration of polarimetric SAR data, which incorporates consistently within a common formalism both area-stretching radiometric compensation and polarization orientation angle shifts compensation, is developed. The validity of the proposed method is demonstrated by using real polarimetric SAR data.

In this work we present a fully parallel SBAS-DInSAR processing chain for the effective and massive generation of Sentinel-1 (S-1) Interferometric Wide Swath (IWS) deformation time-series. The pursued strategy to develop such a parallel processing chain fully benefits from the data acquisition characteristics of the TOPS mode, consisting of a series of bursts that can be considered as separate images to be independently processed. Such a data structure fosters the intrinsic parallelization of the S-1 IWS interferometric data processing, which can automatically be carried out through the exploitation of high-performance distributed computing infrastructures, and of efficient algorithms for tackling the huge data flow provided by the S-1 constellation. In order to demonstrate the capability of the presented S-1 P-SBAS processing chain to deal with the huge amount of data acquired by the S-1 constellation, we process a very extended dataset acquired over Italy from which we compute the mean velocity maps and corresponding deformation time-series.

In this paper we derive a simple model to describe the distortions on focused airborne SAR data rising from the usage of inaccurate external information of the topography of the illuminated area. The model is based on the assumption that an accurate time-domain strategy is applied to focus the data. Moreover, perfect knowledge of the aircraft trajectory is also assumed, in order to better highlight the relation between true topography and airborne SAR focused data. The presented model takes into account the coupling between the trajectory deviations affecting the aircraft flight track and the topography errors. A closed form expression for both the amplitude and phase distortions corrupting the focused signal is provided. To validate the presented analysis some simulation results relevant to a realistic SAR acquisition scenario are finally shown.

About 54% of world population nowadays lives in urban areas and this percentage is expected to increase up to 66% by 2050. Therefore, it is crucial to manage this social and cultural change by collecting, integrating and sharing reliable and open spatial information concerning the urban environments where we leave in. The present availability of huge archives of synthetic aperture radar (SAR) data collected since 1992 by ESA, in conjunction with the available Earth-Observation (EO) optical data, represents a unique possibility to derive valuable information for the understanding of the ongoing urban processes. In this framework, the three-year project financed by the Italian Ministry of Instruction, Research and University, entitled “URBAN GEOmatics for bulk information generation, data assessment and technology awareness” may play a role for the assessment and the development of new replicable methodologies for the study of soil consumption and mobility in urban zones. The paper aims to present some preliminary results achieved during the project, clarifying how the new emerging technologies for managing big EO data are proficient for the investigation of urban processes.

This paper aims at investigating the use of microwave and optical images for the detection and characterization of fire scars in vegetated areas. To cope with this issue, Sentinel-l (S-1) C-band synthetic aperture radar (SAR), and multispectral Sentinel-2 (S-2) multispectral data are used. First, a consolidated fuzzy-based methodology, specifically designed for retrieving burned areas, is considered. Then, an investigation based on the analysis of the maps of backscattering coefficients, as obtained by processing stacks of SAR images, is performed. The outcomes obtained with SAR data are validated by comparison with the results obtained with the optical multispectral data. Presented experiments are related to the Soberanes fires, California, which ignited on July 22,2016, and affected more than 400 square kilometers. In addition, we explore the potential use of stacks of spatial coherence maps generated from pairs of complex-valued SAR images that encompass a fire event to recover new, additional information on the vegetation state and extent of the fire scars. To the purpose, stacks of S-1 SAR images acquired at different polarizations over the Mt. Vesuvius, Italy, in the 2017 summer season has been used. Some preliminary results, which evidence how the variations of the spatial coherence values can be somehow related to a fire and used to recover additional pieces of information, are presented.

In this work we implement a completely automatic interferometric processing chain, based on the well-known advanced DInSAR algorithm referred to as Parallel Small BAseline Subset (P-SBAS), for the generation of Sentinel-l (S-1) Interferometric Wide Swath (IWS) deformation mean velocity maps and time-series of very wide areas, implemented within the Amazon Web Services (AWS) Elastic Cloud Compute environment. Our processing chain consists of the initial data query to the S-1 archive that we created on the AWS S3 storage, then the data transfer to the AWS computing nodes, the data processing and, finally, the transfer of the obtained interferometric results back to the original S3 storage. In order to demonstrate the capability of the implemented Cloud-based processing chain to deal with massive amount of data, we focus our analysis on the whole Italian territory by processing all the available data acquired both from ascending and descending orbits within the October 2014 - March 2017 time interval. As final result we combine the retrieved LOS displacements in order to compute the mean velocity maps and time-series of the vertical and East-West surface deformation components.

This letter aims to investigate the applicability of the hybrid stripmap-ScanSAR differential synthetic aperture radar interferometry to data collected by the X-band RADAR sensors of the Italian COSMO-SkyMed (CSK) constellation. The presented analysis relies on the experiments carried out on two sets of stripmap and ScanSAR CSK images collected over the Chuquicamata copper mine in Chile and the Mojave Desert in Southern California, respectively. As a result of our investigation, a spurious phase modulation has been recognized in the generated hybrid stripmap-ScanSAR interferograms along the azimuth direction. This effect is due to the specific staggered topology of the used active phased array antennas that introduces half-wavelength residuals during the electronic antenna steering in the elevation plane. A straightforward solution to compensate for such spurious phase modulation effects has been provided, and the achieved results demonstrate its effectiveness. The outcomes of our analysis may be relevant in the perspective of the future CSK Second Generation Program.

We present a simple solution for the phase-preserving focusing of Terrain Observation with Progressive Scan (TOPS) Sentinel-1 data. The proposed algorithm focuses each raw data burst independently. In particular, after a conventional range data compression, it is based on a modification of the two-step processing approach formerly developed to focus spotlight and hybrid stripmap/spotlight SAR data. The presented results relevant to a real Sentinel-1 data acquired over central Italy confirm the effectiveness of the proposed approach.

In this work, a new technique for the correction of a digital elevation models (DEM) and the retrieval of the ground mean displacement rate of an investigated area, which relies on the analysis of a sequence of wrapped multi-master differential SAR interferograms, is presented. The method consists in finding the solution of a non-linear optimization problem that does not require the preliminary execution of any phase unwrapping operation. Therefore, the achievable estimates are not disturbed by phase unwrapping (PhU) errors. Noteworthy, the estimation of the ground displacement rate may reveal (in most cases) particularly suitable for retrieving geophysical information on the sources responsible for the observed deformations. Furthermore, the estimates of the DEM errors and the ground displacement rate can be used within advanced DInSAR processing chains for the retrieval of surface deformation time-series, with the aim to facilitate phase unwrapping operations and discriminate the desired signals (due to deformation) from residual phase artifacts. The first experimental results, carried out by applying the proposed method to stacks of simulated interferograms, demonstrate the validity of the proposed approach.

In this work we present an advanced interferometric processing chain, which is based on the DInSAR algorithm referred to as Parallel Small BAseline Subset (P-SBAS) approach, for the massive processing of SENTINEL-1 (S1) Interferometric Wide Swath (IWS) data. The P-SBAS S1 processing chain produces surface deformation time series, and the relevant mean velocity maps, in automatic and systematic way by efficiently exploiting Cloud Computing infrastructures, thus allowing us to perform DInSAR analyses at very large scale in reduced time frames. As experimental results, the overall mean deformation velocity map relevant to the Central and Southern Italy zone (from Lazio to Sicily), generated by processing in parallel about 300 S1 acquisitions within the Amazon Web Services Cloud Computing platform, is presented. Moreover, the displacement time series of some pixels located in volcanic deforming areas such as the Campi Flegrei Caldera (Napoli Bay area) and the Mt. Etna (Sicily) are shown.

We present a solution for simplifying a recently proposed two-step processing technique that allows to retrieve the constant phase offset present in the unwrapped synthetic aperture radar (SAR) interferograms by exploiting an external, even low-accuracy, digital elevation model (DEM) of the illuminated area and without using corner reflectors. In particular, we show in this letter that the second processing step, namely, the slope-topography-based estimate, can be avoided without impairing the accuracy of the final phase offset estimate. To this aim, we introduce a simple modification to the first step, referred to as phase-based estimate, by considering the vertical bias of the available external DEM as the second unknown parameter in the carried out estimation. The simplified algorithm is very easy to implement and is particularly suitable for airborne SAR interferometry. It has been tested on real airborne SAR data and the obtained results show that the achieved accuracy is the same or better than that achieved through the original two-step approach.

This work aims to describe the characteristics and the status of development, including the results of a first preliminary testing campaign, of a low frequency airborne imaging radar developed in Italy for the Italian Space Agency.

In this work we present an efficient interferometric processing chain, based on the advanced DInSAR algorithm referred to as Parallel Small BAseline Subset (P-SBAS), for the generation of Sentinel-1A (S1A) Interferometric Wide Swath deformation time-series, which is able to exploit distributed computing architectures. The presented S1A P-SBAS processing chain has been successfully implemented within the ESA Geohazard Exploitation Platform to provide an on-demand automatic service for the unsupervised generation of P-SBAS displacement time-series. To give an idea of the effectiveness of the presented S1A processing chain, as a preliminary result we show a 12-days interferometric analysis at continental scale, carried out by exploiting 150 S1A interferometric pairs acquired over Europe for an overall covered area of about 7,500,000 km2.

The paper presents the results of an experiment carried out with a hybrid along-track (AT) / across-track (XT) interferometric Synthetic Aperture Radar (SAR) configuration in order to simultaneously estimating the velocity and the elevation pattern of the illuminated sea surface. In particular, the experiment has been carried out in 2013 with the X-band InSAeS4 airborne SAR system, which is equipped with three antennas installed in such a way to allow the simultaneous presence of three AT baselines and three XT baselines. The test-site is the coastline stretch of the Campania region, in the South of Italy, close to the Volturno river.

We present a case study on the migration to a Cloud Computing environment of the advanced differential synthetic aperture radar interferometry (DInSAR) technique, referred to as Small BAseline Subset (SBAS), which is widely used for the investigation of Earth surface deformation phenomena. In particular, we focus on the SBAS parallel algorithmic solution, namely P-SBAS, that allows the production of mean deformation velocity maps and the corresponding displacement time-series from a temporal sequence of radar images by exploiting distributed computing architectures. The Cloud migration is carried out by encapsulating the overall P-SBAS application in virtual machines running on the Cloud; moreover, the Cloud resources provisioning and configuration phases are implemented in an automatic way. Such an approach allows us to preserve the P-SBAS parallelization strategy and to straightforwardly evaluate its performance within a Cloud environment by comparing it with those achieved on a HPC in-house cluster. The results we present were achieved by using the Amazon Elastic Compute Cloud (EC2) of the Amazon Web Services (AWS) to process SAR datasets collected by the ENVISAT satellite and show that, thanks to the Cloud resources availability and flexibility, large DInSAR data volumes can be processed through the P-SBAS algorithm in short time frames and at reduced costs. As a case study, the mean deformation velocity map of the southern California area has been generated by processing 172 ENVISAT images. By exploiting 32 EC2 instances this processing took less than 17 hours to complete, with a cost of USD 850. Considering the available PB-scale archives of SAR data and the upcoming huge SAR data flow relevant to the recently launched (April 2014) Sentinel-1A and the forthcoming Sentinel-1B satellites, the exploitation of Cloud Computing solutions is particularly relevant because of the possibility to provide Cloud-based multi-user services allowing worldwide scientists to quickly process SAR data and to manage and access the achieved DInSAR results.

This paper describes a general-purpose parallel scheme for efficiently focusing synthetic aperture radar (SAR) data on multicore-based shared-memory architectures. The rationale of the proposed tiling-based parallel focusing model is first discussed, and then, its implementation structure is illustrated. The adopted parallel solution, which is based on a canonical processing pattern, exploits a segmented-block-based approach and works successfully on data acquired by different spaceborne SAR platforms. Insofar as a significant portion of the focusing algorithm is amenable to tiling, our approach decomposes the problem into simpler subproblems of the same type, also providing a suitable mechanism to explicitly control the granularity of computation through the proper specification of the tiling at the different stages of the algorithm itself. Relevant implementation makes use of multithreading and high-performance libraries. Achievable performances are then experimentally investigated by quantifying the benefit of the parallelism incorporated into the prototype solution, thus demonstrating the validity of our approach. Accordingly, canonical performance metrics have been evaluated, and the pertinent scalability has been examined on different multicore architectures. Furthermore, in order to emphasize the practical ability of the proposed parallel model implementation to efficiently deal with data of different SAR sensors, a performance analysis has been carried out in different realistic scenarios including data acquired by the Envisat/ASAR, RADARSAT-1, and COSMO-SkyMed platforms.

Often scientific applications are characterized by complex workflows and large datasets to manage. Usually, these applications run in dedicated high performance computing centers with low-latency interconnections which require a consistent initial cost. Public and private cloud computing environments, thanks to their features such as customized computing environments, flexibility, and elasticity represent a valid alternative with respect to HPC clusters in order to minimize costs and optimize processing. In this paper the migration of an advanced Differential Synthetic Aperture Radar Interferometry (DInSAR) methodology for the investigation of Earth surface deformation phenomena to the Amazon Web Services (AWS) cloud computing environment is presented. Such a technique which is referred to as Parallel Small Baseline Subset (P-SBAS) algorithm allows producing mean deformation velocity maps and the corresponding displacement time-series from a temporal sequence of radar images. Moreover, an experimental analysis aimed at evaluating the P-SBAS algorithm parallel performances which are achieved within the AWS cloud by exploiting two different families of instances and by taking into account different I/O and network bandwidth configurations is presented.

In the present radar remote sensing scenario, the huge availability of SAR data acquired by a number of satellite constellations is a key point for investigating Earth's surface at large scale. In particular, techniques as Differential SAR Interferometry (DInSAR) could strongly benefit from such data availability for measuring ground displacement at global scale. However, to efficiently and effectively exploit such amount of Big DInSAR Data, the development of new algorithms and techniques as well as the use of appropriate High Performance Computing infrastructure is becoming mandatory. In this work we present a number of case studies based on the recently proposed Parallel Small BAseline Subset (P-SBAS) DInSAR algorithm, that allows generating surface displacement maps in automatic and unsupervised way, aimed at providing effective solutions to the increased DInSAR data volume processing.

A general-purpose parallel scheme for efficiently focusing synthetic aperture radar (SAR) data on multicore-based shared-memory architectures is presented. The proposed parallel solution is based on a canonical processing pattern that exploit a segmented-block-based approach, and it works successfully on data acquired by different SAR platforms. Insofar as significant portion of the focusing algorithm is amenable to tiling, our approach decomposes the problem into simpler subproblems of the same type, also providing a suitable mechanism to explicitly control the granularity of computation through the proper specification of the tiling. Relevant implementation makes use of multithreading and high-performance libraries. Achievable performances are then experimentally investigated by quantifying the benefit of the parallelism incorporated into the prototype solution, thus demonstrating the validity of our approach.

A parallel model for the solution of the computationally intensive multichannel phase unwrapping (MCh-PhU) problem is proposed. Emphasis is placed on those methodological and practical aspects, which lead to a parallel reformulation of the Extended Minimum Cost Flow (EMCF) algorithm. Thus, a novel dual-level parallel computational model, in which the parallelism is hierarchically implemented at two different (i.e., process and thread) levels, is presented. The validity of our approach has been demonstrated through a series of experiments that have revealed a significant speedup. Therefore, the attained high-performance prototype is suitable for the solution of large-scale phase unwrapping problems in reasonable time frames, with a significant impact on the systematic exploitation of the existing, and rapidly growing, large archives of SAR data.

In order to retrieve information about the sea surface velocity in coastal Mediterranean regions from Synthetic Aperture Radar (SAR) data, in this work we exploit spaceborne as well as airborne data. In particular, we apply two different processing techniques. The first one exploits a single SAR image, whereas the second one, referred to as Along-Track SAR Interferometry, exploits SAR data pairs. The first technique has been applied to spaceborne ENVISAT-1 SAR data, whereas the second one has been applied to airborne data acquired by the Italian InSAeS4 X-band SAR system.

This work provides an overview on the evolution of the Differential Synthetic Aperture Radar Interferometry (DInSAR) scenario in the last 25 years, from the ERS-1 to the SENTINEL-1 mission, with particular emphasis given to the advanced DInSAR methodologies. Moreover, the discussion is also focused on the observed drastic requirements increase related to the collected SAR data archives as well as on the developments of new solutions for an efficient processing of these data and the analysis of the achieved results.

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.

This paper presents the integration of the advanced Differential SAR Interferometry (DInSAR) algorithm referred to as Parallel Small BAseline Subset (P-SBAS) within the ESA's Grid Processing on Demand (G-POD) environment in the framework of ESA Geohazards Exploitation Platform (GEP). The aim of this activity is to set up a scientific service that allows, in unsupervised manner, the generation of SBAS-DInSAR products, such as surface mean deformation velocity map and the corresponding time series. In particular, such a web tool is aimed at efficiently exploit the huge ESA's SAR data archives (ERS and ENVISAT), giving a support to scientific users, especially those non-expert of SAR data processing, for interferometric analysis in a short time frame.

Synthetic Aperture Radar Interferometry (InSAR) exploits the phase differences (interferogram) of SAR data pairs relevant to the same illuminated area and received by slightly different look angles. Within the InSAR processing chain, it is necessary to calculate a proper phase offset (PhOff) value to add to the unwrapped SAR interferogram. To this aim, different algorithms have been proposed in the literature, which typically exploit the external topography information provided by proper Ground Control Points (GCPs) relevant to the observed scene. In this paper we present a Maximum Likelihood (ML) approach for the InSAR PhOff estimation. It is based on the use of GCPs represented either by Corner Reflectors (CRs) or by the pixels of an available external Digital Elevation Model (DEM) of the observed area. The effectiveness of the proposed approach is assessed on simulated data.

In this paper, we investigate the potential of Cosmo-SkyMed (CSK) platform to offer a basis for the generation of hybrid ScanSAR-Stripmap differential interferograms to be used for the retrieval of long-lasting time-series of deformation via advanced multi-temporal DInSAR approaches. In particular, this study represents a first preliminary step in the perspective to extend the well-known small baseline subset (SBAS) approach to the use of such hybrid CSK datasets, thus expanding what already proposed in literature for ENVISAT data. Experimental results were achieved by processing hybrid stripmap-ScanSAR SAR data pairs collected over a mine facility in Chile; achieved results confirm the validity of applied methods.

InSAeS4 is an Italian X band airborne SAR system, recently upgraded to a multi-antenna interferometric configuration. The system is able to acquire single pass across-track and along-track multi-baseline data with different acquisition modes characterized by different geometric resolutions and across-track swaths. In recent work we have analyzed the high resolution InSAeS4 products (obtained by transmitting 400 MHz bandwidth pulses), characterized by the narrowest across-track swath (1.5 Km wide). In this work, we focus on the InSAeS4 products characterized by the widest across-track swath, at expenses of the lowest geometric resolution. To this aim we show some results achieved with an interferometric dataset acquired by the system in 2013 over the Napoli area, Italy.

The application of an integrated monitoring tool to assess and understand the effects of annually occurring forest fires is presented, with special emphasis to Mediterranean and Temperate Continental zones of Europe. The distinctive features of the information conveyed by optical and microwave remote sensing data have been firstly investigated, and pertinent information have been subsequently combined to identify burned areas at the regional scale. We therefore propose a fuzzy-based multisource framework for burned area mapping, in order to overcome the limitations inherent to the use of only optical data (which can be severely affected by cloud cover or include low albedo surface targets). The relevant experimental validation has been carried out on an extensive area, thus quantitatively demonstrating how our approach successes in identifying areas affected by fires. Furthermore, the proposed methodological framework can also be profitably applied to Sentinel (optical and SAR) data.

The current Remote Sensing scenario is characterized by the availability of huge archives of SAR data that are going to increase with the advent of Sentinel-1 satellites. The effective exploitation of this large amount of data requires both adequate computing resources as well as advanced algorithms able to properly exploit such facilities. In this work we discuss the migration of the DInSAR technique referred to as Parallel Small BAseline Subset (P-SBAS), which is used for Earth's surface deformation investigation, to the Amazon Web Services (AWS) public Cloud Computing environment. An experimental analysis aimed at evaluating the P-SBAS scalable performances that are achieved within the Cloud environment is presented. The achieved results show very good parallel performances and allow us to identify the major bottlenecks that can hamper such behavior when the amount of data to process highly increases. Accordingly, we present an advanced P-SBAS implementation that is designed to overcome the identified bottlenecks. The experimental analysis is carried out by processing both Envisat and COSMO-SkyMed datasets and by exploiting both a High Performance Computing cluster as well as AWS public Cloud.

In the theoretical purview of the discrete Calculus, a rigorous gradient-based formulation of the multichannel phase unwrapping (MCh-PhU) problem is systematically established in terms of discrete differential operators, which are defined by the topology of the intrinsically discrete spaces upon which they act, thus capturing the essential topological character of the problem within a suitable matrix formalism and providing interesting implications. Within this methodological framework, the extended minimum cost flow (EMCF) algorithm, which provides an effective strategy aimed at solving the MCh-PhU problem, is revised, and its computational structure is analyzed. A parallel formulation of the computational-intensive EMCF algorithm is then presented. Emphasis is placed on the methodological and practical aspects leading to a novel dual-level parallel computational model in which the parallelism is hierarchically implemented at two different levels. Performance evaluation relevant to the implemented prototype solution is also carried out, thus quantifying the benefit of parallelism at different levels. The significant experimentally achieved speedup demonstrates the validity of our approach. As a result, the attained parallel prototype enables the large-scale solution of the MCh-PhU problem in a reasonable time frame, with a great impact on systematic exploitation of the available SAR archives.

We present in this paper a solution to drastically improve the deformation time-series retrieval capability of the small baseline differential SAR interferometry (DInSAR) processing chain based on the cascade of the extended minimum cost flow (EMCF) phase unwrapping method and of the small baseline subset (SBAS) inversion technique. This improvement relies on the inclusion of two preprocessing steps implementing an effective noise-filtering operation and an efficient interferogram selection procedure, respectively. The former step filters out the noise affecting the phase components of a redundant set of conventional multi-look small baseline interferograms. This is achieved by solving, for each pixel, a nonlinear minimization problem based on computing the wrapped phase vector that minimizes the weighted circular variance of the phase difference between the original and noise-filtered interferograms. This technique is very easy to implement because it does not require any pixel selection step to be applied to the exploited full-resolution SAR images, and it has no need of any a priori information on the statistics of the complex-valued SAR images. The latter step, implementing the interferogram selection procedure, is carried out via a computationally efficient simulated annealing algorithm and allows identifying the optimum set of previously filtered small baseline interferograms to be used as input for the original EMCF-SBAS processing chain by maximizing the (average) coherence values. The presented results, achieved by processing three data sets collected by the ENVISAT ASAR sensor over the Abruzzi region (Central Italy), Mt. Etna volcano (South Italy), and Yellowstone Caldera (WY, USA), demonstrate the effectiveness of the proposed advanced EMCF-SBAS processing chain.

Digital elevation model (DEM) generation through interferometric processing of synthetic aperture radar (SAR) data requires the calculation of a constant phase offset present in the unwrapped interferograms. This operation is usually carried out by exploiting the external information provided by GPS measurements in correspondence of corner reflectors (CRs) properly deployed over the illuminated area. This is, however, expensive in terms of cost and time. Moreover, deployment of CRs along with the corresponding in situ GPS measurements can be difficult (if not impossible) in unfriendly areas or in natural disaster scenarios. To circumvent these limitations, we address in this work the estimation of the required phase offset by exploiting a low-accuracy external DEM, without using CRs. More specifically, a two-step approach is proposed. The first step exploits the synthetic phase computed by means of the external DEM and represents a straightforward extension of the procedure that is usually applied in the presence of CRs. Subsequently, in order to refine the achieved solution, a second step is introduced. It is based on a least squares approach that properly exploits the difference between the available low-accuracy DEM and the interferometric DEM generated by means of the phase offset value roughly estimated through the first step. The presented approach is very easy to implement and allows us to achieve an accurate and fast estimate of the needed phase offset, even in the presence of an external DEM affected by a vertical bias and/or a planar shift. The algorithm performances improve in the presence of a large variation of the look angle, as it generally happens in airborne systems. On the other side, the effectiveness of the algorithm may be impaired by the possible presence of artifacts in the unwrapped interferograms, such as those due to the residual motion errors typical of repeat-pass airborne SAR scenarios. Accordingly, the proposed solution is particularly suitable for single-pass interferometric airborne SAR systems, as demonstrated through the presented experimental results achieved on real data.

The advanced Differential SAR Interferometers (DInSAR) methodologies are widely used for the investigation of Earth's surface deformation phenomena. In particular, the advanced DInSAR approach referred to as Small Baseline Subset (SBAS) technique is able to produce deformation velocity maps and the corresponding displacement time-series from a temporal sequence of space borne SAR acquisitions. Considering the already huge SAR data archives as well the upcoming massive data flow coming from the SENTINEL satellite constellation, cloud computing can be a valid solution to carry out DInSAR analyses thanks to its scalability and flexibility features. In this paper, the focus is given on the migration of the whole parallel version of the SBAS technique, namely P-SBAS, to a cloud environment by taking into account different parameters that influence processing time. Experimental tests that have been performed using both private and public cloud are also presented.

TELAER is an Italian airborne X-Band Synthetic Aperture Radar (SAR) system recently upgraded to the interferometric mode thanks to an Italian National Research Council (CNR) funding. This system upgrading has been completed at the beginning of 2013 with a flight-test campaign carried out over the Napoli area, Italy. In this paper we present some results relevant to a single-pass interferometric dataset acquired during these flight-tests.

Synthetic Aperture Radar Interferometry (InSAR) allows the generation of Digital Elevation Models (DEMs) exploiting the phase difference (interferogram) of SAR data pairs relevant to the same illuminated area and received by slightly different look angles. Within the processing chain leading from the acquired SAR data pair to the final InSAR DEM, it is necessary to calculate a proper phase offset value to add to the unwrapped SAR interferogram. Moreover, in the airborne case, it can be necessary also the compensation of the residual motion errors not perfectly removed by the exploited Motion Compensation (MOCO) procedures during the focusing step. In this paper we present a novel algorithm for joint InSAR phase offset calculation and residual motion error compensation based on the use of an external low accuracy DEM. The effectiveness of the proposed algorithm is assessed on real data acquired by the multi-antena airborne OrbiSAR and TELAER system, both operating at X-band.

In this work we present a detailed analysis of the co-seismic deformation signals associated with the 2012 Emilia (Northern Italy) seismic crises through spaceborne Differential Synthetic Aperture Radar (SAR) Interferometry. In particular, we provide a general picture of the ground deformation fields relevant to the Ml 5.9 and Ml 5.8 main shocks, by generating four co-seismic displacement maps from X-band COSMO-SkyMed and C-band RADARSAT-1/2 SAR data, collected by descending orbits over the investigated area. Subsequently, we perform an analytical modelling of the seismic events by exploiting the RADARSAT-2 displacement map through a finite dislocation fault, in order to get more information about the earthquake source locations and their geometries.

We exploit advanced Differential SAR Interferometry (DInSAR) techniques for enhanced analyses of slope instability phenomena. In particular, we focus on the Ivancich urban area, in the worldwide known historic town of Assisi, Central Italy, affected by a deep-seated, slow moving active landslide for which geological and geotechnical data are also available. Furthermore over the landslide site, large datasets of SAR acquisitions collected in the last two decades by the C-band ERS-1/2 and ENVISAT SAR sensors, and by the X-band radars of the COSMO-SkyMed constellation are available. In our study, we applied the advanced DInSAR technique referred to as Small BAseline Subset (SBAS), benefiting from its ability to generate deformation time series at full spatial resolution from multi-sensor SAR data, that allowed us to characterize the temporal and spatial pattern of ground deformations induced by the landslide. Furthermore, we experimented an innovative approach for landslide modeling, based on the integration of DInSAR measurements with conventional geological and geotechnical data used in landslide studies.

This paper proposes a parallel model for the Differential Interferometry Synthetic Aperture Radar approach referred to as Small BAseline Subset (SBAS) algorithm. This new computational model has been designed to be specifically exploited within the emerging cloud computing environments. An experimental analysis, involving two different case studies, has been carried out to demonstrate the effectiveness of the methodology. The major novelty of the proposed Parallel SBAS (P-SBAS) model consists in the capability of processing large SAR data sets in reasonable time-frames. This key feature may be of great impact not only for hazard monitoring and risk mitigation activities but also for data sharing and knowledge spreading within the scientific community.

We present an enhanced version of the SBAS-DInSAR processing chain, which complements the conventional SBAS codes with a simple noise-filtering procedure that allows us to mitigate noise artifacts affecting the sequence of multi-look (multi-temporal) small baseline interferograms used within the SBAS inversion. We demonstrate that the use of such a noise-filtering algorithm permits to significantly improve the quality of the mean deformation velocity maps and displacement time-series retrieved via the SBAS-DInSAR processing chain, thus promoting more detailed analyses of the detected deformation phenomena. The overall effectiveness of the enhanced SBAS-DInSAR approach is confirmed by the experimental results achieved by applying the proposed method to two sets of SAR data collected by the ERS-1/2 and ENVISAT sensors over the Mt. Etna volcano (Southern Italy) and Yellowstone caldera (Wyoming, U.S.).

We carry out a quantitative assessment of the accuracy of the advanced Differential SAR Interferometry (DInSAR) approach referred to as Small BAseline Subset (SBAS) in mapping deformation phenomena affecting active volcanic areas, by exploiting SAR data acquired by the “first” and “second” generation SAR sensors, and by comparing the achieved results with independent geodetic measurements, the latter assumed as reference. In particular, we analyze the impact that the different wavelengths and looking geometries may have in the SBAS-DInSAR measurement retrieval depending on the radar system. To this aim, we consider SAR images acquired by the ERS-1/2 and ENVISAT (C-band) as well as COSMO-SkyMed (X-band) sensors (i.e., by the “first” and “second” generation SAR sensors, respectively) over the Campi Flegrei caldera (Southern Italy) and the in-situ displacement measurements provided by the continuous GPS stations deployed in the area. The performed comparative analysis reveals a general good agreement between DInSAR and geodetic data; it also shows that the single displacement measurements have a sub-centimetric accuracy with a standard deviation of about 0.5 cm.

In this work we focus on the Precise Topography- and Aperture-dependent algorithm, briefly PTA, proposed in the recent literature to remove the residual errors still affecting airborne SAR data focused with standard two-step Motion Compensation algorithms. More specifically, we first show that the azimuth-to-frequency mapping used by the PTA procedure is not fully appropriate, because it does not account for the presence of the residual motion errors themselves. Then, we show how to calculate the correct azimuth-to-frequency mapping able to fully account for the presence of the residual motion errors. Finally, we show how to modify the original PTA in such a way to properly exploit the correct azimuth-to-frequency mapping previously discussed.

Synthetic Aperture Radar Interferometry (InSAR) allows the generation of Digital Elevation Models (DEMs) exploiting the phase difference (interferogram) of SAR data pairs relevant to the same illuminated area and received by slightly different look angles. Within the processing chain leading from the acquired SAR data pair to the final InSAR DEM, it is necessary to calculate a proper phase offset value to add to the unwrapped SAR interferogram. To this aim, different algorithms have been proposed in the literature: most of them make use of very accurate information on the position of Ground Control Points (GCPs), such as Comer Reflectors (CRs), properly deployed over the observed scene. In this paper we present a novel algorithm for InSAR phase offset calculation without using CRs. The effectiveness of the proposed algorithm is assessed on real data acquired by the multi-antenna airborne OrbiSAR system operating at X-Band.

We investigate the capability improvement of the advanced differential interferometric synthetic aperture radar (DInSAR) techniques to map deformation phenomena affecting urban areas by exploiting multitemporal SAR data acquired by the new X-band sensors with respect to those of the previous C-band systems. In particular, we perform a comparative analysis of the deformation time-series retrieved by applying the full-resolution Small BAseline Subset DInSAR technique to selected sequences of SAR data acquired by the ENVISAT and RADARSAT-1 sensors (both operating at C-band) and by the X-band radar systems onboard the SAR sensors of the COSMO-SkyMed (CSK) constellation. This study, focused on the city of Napoli (Italy), allows us to quantify the dramatic increase of the DInSAR coherent pixel density achieved by exploiting the high-resolution X-band CSK SAR images (a few meters), resulting in an improvement factor of about 320% and 550%, with respect to the RADARSAT-1 and ENVISAT products, respectively. This improvement permits us to analyze nearly all the structures located within the investigated urbanized area and, in many cases, also portions of the same building. The improved coherent pixel spatial densities, combined with the reduced revisit times of the new X-band SAR missions, allow us to significantly increase the effectiveness of the advanced DInSAR methodologies, further extending the role of those Earth Observation data in the development of monitoring scenarios.

We present some of the main advances on long term deformation time series analysis achieved thanks to the 10 years of operation of ENVISAT ASAR sensor. This is done by considering a number of selected case studies, which are mostly based on the use of the Small BAseline Subset (SBAS) technique. In particular, we show how we benefit from the ENVISAT data availability to extend the ERS time series, thus allowing us to obtain almost 20 years of deformation history. Moreover, we demonstrate how the multi-mode capability of ENVISAT permits improving the temporal sampling in SBAS time series. Finally, we introduce a novel technique, tested on ENVISAT data, to generate displacement time series in areas affected by large deformation phenomena.

We investigate the capability improvement of the DInSAR techniques to map deformation phenomena affecting urban areas, by performing a comparative analysis of the deformation time series retrieved by applying the full resolution Small BAseline Subset (SBAS) DInSAR technique to selected sequences of SAR data acquired by the ENVISAT, RADARSAT-1 and COSMO-SkyMed (CSK) SAR data. The presented study, focused on the city of Napoli (Italy), allows us to quantify the dramatic increase of the DInSAR coherent pixel density achieved by exploiting the high resolution X-Band CSK SAR images with respect to the RADARSAT-1 and ENVISAT products, respectively; this permits us to analyze nearly all the structures located within the investigated urbanized area and, in many cases, also portions of a same building.

We perform a quantitative assessment of the accuracy of Differential SAR Interferometry (DInSAR) time series in volcanic areas, retrieved through “first” and “second generation” SAR data. In particular, we analyze the impact that the wavelengths and looking geometries may have in the DInSAR measurement retrieval depending on the radar system. To this aim, we focus on the DInSAR algorithm referred to as Small BAseline Subset (SBAS) to generate mean deformation velocity maps and corresponding time series starting from sequences of SAR images. Moreover, we consider collections of SAR data acquired by the ERS-1/2 and ENVISAT (C-band), and COSMO-SkyMed (Xband) sensors over the volcanic area of the Campi Flegrei caldera, Southern Italy. We invert these SAR data sequences through the SBAS-DInSAR technique, thus obtaining C- and X- band deformation time series that we compare to continuous GPS measurements, the latter assumed as reference. The achieved results provide, in addition to a clear picture of the surface deformation phenomena already occurred and occurring in the selected case study, relevant indications for the analysis of the SBAS-DInSAR time series accuracies in volcanic areas passing from the first to second generation SAR sensors.

We propose an innovative Phase Unwrapping (PhU) approach to unwrap a sequence of full-resolution multi-temporal differential interferograms through a Region Growing (RG) strategy. The algorithm is designed to handle noisy interferograms to carry out effective phase unwrapping process concurrently of large single-look differential interferograms. The key idea of our algorithm is to exploit a set of well unwrapped pixels (seed points) by applying existing multi-temporal differential technique and propagate the solution to successfully unwrap the neighbouring wrapped pixels (candidate points). To this aim, phase unwrapping of the candidate point network is effectively performed exploiting the Extended Minimum Cost Flow (EMCF) algorithm applied to a Constrained Delaunay Triangulation. The proposed algorithm has been applied to a set of 116 ERS-1/2 and ENVSAT SAR images acquired over the city of Rome (Italy) in 1992-2010. The achieved results demonstrate the effectiveness of the innovative proposed PhU approach.

This paper describes the capabilities of the TELAER X-Band airborne SAR system, which has been upgrading according to a Italian National Research Council (CNR) funding. Such a system upgrading consists first of all in the realization of a multi-antenna system able to carry out, simultaneously, Across-Track (XT) and Along-Track (AT) SAR Interferometry (InSAR). Moreover, the system upgrading is also aimed at improving the performances achievable in repeat-pass applications, such as Differential SAR Interferometry, (DInSAR), which require very precise measurement of the tracks described during the flight by the SAR antennas.

We present an efficient algorithm to mitigate noise effects in differential interferograms by exploiting the temporal relationships among a redundant set of small baseline interferograms. The core of the proposed technique is represented by the estimation of the (wrapped) filtered phase values associated to the available SAR acquisitions. This result is achieved by using conventional multi-look interferograms by applying a non-linear maximization procedure that exploits only phase information, and does not require any assumption on the statistics of the complex-valued SAR images involved in the interferogram generation. Subsequently, from the retrieved phase images, a filtered version of the original interferograms can be simply reconstructed and used to generate deformation time series by the conventional Small BAseline Subset (SBAS) approach. The experimental results, achieved by applying the proposed approach to a dataset consisting in 39 SAR data acquired from 2002 to 2010 over the Abruzzi (Central Italy) area confirm the effectiveness of the proposed method.

In this paper we discuss the extension of an algorithm for efficient processing of SAR data acquired in sliding spotlight mode based on a two-step approach. The two-step spotlight data processing procedure, already introduced in the literature, is a very simple and effective procedure because it is based on a plain use of classical (i.e. stripmap mode) SAR processing codes. The algorithm here proposed extends the two-step approach in such a way t account for the presence of squint. Experiments have been carried out on simulated data to clarify the rationale of the method and to confirm its validity. Results obtained by processing real data of the COSMO-SKYMED constellation are also presented.

We present an efficient space-time phase unwrapping (PhU) algorithm allowing us to analyze sequences of multitemporal full resolution differential Synthetic Aperture Radar (SAR) interferograms for the generation of deformation time-series. The core of the proposed technique is represented by the extended minimum cost flow (EMCF) PhU algorithm. Our method is based on a joint analysis of the spatial and temporal relationships among a set of properly selected multitemporal differential interferograms. More specifically, the key idea is to split the complex MCF network problem into that of simpler sub-networks. Accordingly, we start by identifying and solving a primary network that involves a proper selection of coherent pixels of the computed interferograms, representing the backbone structure of the overall network. Subsequently, this result is applied for constraining the solution of the sub-networks connected to the primary one, involving the entire set of analyzed pixels. This task is achieved by solving a constrained optimization problem based on the computation of a Constrained Delaunay Triangulation in the Azimuth/Range domain. The experimental results, achieved by applying the proposed approach to a dataset consisting of European Remote Sensing (ERS) SAR data acquired, from June 1992 to August 2007 over the Napoli (Italy) bay area, confirm the effectiveness of the proposed PhU approach..

We investigate the deformation of Mt. Etna volcano (Italy) by exploiting the advanced Differential Synthetic Aperture Radar Interferometry (DInSAR) technique referred to as the Small BAseline Subset (SBAS) algorithm. In particular, we take advantage of the multi-sensor data processing capability of the SBAS algorithm which allows us to generate Mt. Etna mean deformation velocity maps and the corresponding time series in the last eighteen years. To achieve this task we exploit different set of SAR data collected by the European (ERS-1/2, ENVISAT) satellites in the 1992-2010 time interval, and by the Italian COSMO-SkyMed constellation during 2009-2010 period. We also benefit from the availability of ERS-ENVISAT multi-orbit (ascending and descending) data in order to discriminate the vertical and East-West components of the volcano edifice displacements and generate the relevant time series. Finally, we evidence how the higher spatial resolution and denser temporal sampling of the COSMO-SkyMed data, with respect to the European satellites, permit to follow with more details the complex deformative pattern which has characterized the volcano in the last two years.

We analyze the temporal evolution of the deformation affecting the Yellowstone (Wyoming, U.S) complex volcanic system in the 1992-2010 time period. This work represents, at our knowledge, the first attempt at retrieving through differential synthetic aperture radar (DInSAR) techniques long-term deformation time-series at the Yellowstone Caldera. This is due to the presence of severe temporal decorrelation noise in the differential interferograms, which limit the overall performances of the DInSAR technique, especially with respect to phase unwrapping (PhU) operations. Accordingly, to increase the number of the investigated points in low coherent zones, we complement PhU operations with a space-time region growing method. The presented results, obtained by applying the Small Baseline Subset (SBAS) DInSAR technique to a data set composed of 21 ERS and 22 ENVISAT SAR images, clearly demonstrate the validity of the proposed method.

Time-series interferometric synthetic aperture radar (InSAR) methods estimate the spatiotemporal evolution of deformation over large areas by incorporating information from multiple SAR interferograms. Persistent scatterer (PS) and small baseline (SB) methods, which identify areas where the surface is least affected by geometric and temporal decorrelation, represent two families of time-series InSAR techniques to study successfully a wide spectrum of ground deformation phenomena worldwide. However, little is known comparatively about the performance of PS and SB techniques applied to the same region. Here, we compare quantitatively and cross validate the time-series InSAR results generated using two representative algorithms-the maximum likelihood PS method and the small baseline subset algorithm-in selected test sites, over the San Francisco Bay Area imaged by European Remote Sensing (ERS) sensors during 1995-2000. We present line of sight (LOS) velocities and deformation time series using both techniques and show that the root mean squared differences of the estimated mean velocities and deformation from each method are about 1 mm/year and 5 mm, respectively. These values are within expected noise levels and a characteristic of the pixel selection parameters for both the time-series techniques. We validate our deformation estimates against creep measurements from alignment arrays along the Hayward Fault and show that our estimates agree to within 0.5 mm/year LOS velocity and 1.5 mm LOS displacement.

We exploit the amplitude information of a sequence of synthetic aperture radar (SAR) images, acquired at different times, in order to generate displacement time-series in areas characterized by large and/or rapid deformation, the size of which is on the order of the image's pixel dimensions. We follow the same rationale of the Small BAseline Subset (SBAS) differential SAR interferometry (DInSAR) approach, by coupling the available SAR images into pairs characterized by a small separation between the acquisition orbits. We exploit the amplitudes of the selected image pairs in order to calculate the relative across-track (range) and along-track (azimuth) pixel-offsets (PO). Finally, we apply the SBAS inversion strategy to retrieve the range and azimuth displacement time-series. This approach, referred to as pixel-offset (PO-) SBAS technique, has been applied to a set of 25 ENVISAT SAR observations of the Sierra Negra caldera, Galápagos Islands, spanning the 2003-2007 time interval. The retrieved deformation time-series show the capability of the technique to detect and measure the large displacements affecting the inner part of the caldera that, in correspondence to the October 2005 eruption, reached several meters. Moreover, by comparing the PO-SBAS results to continuous GPS measurements, we estimate that the accuracy of the PO-SBAS time-series is on the order of 1/30th of a pixel for both range and azimuth directions.

We present an algorithm aimed at correcting satellite orbit information for the generation of differential SAR interferometry (DInSAR) deformation time-series. Our approach exploits small baseline differential interferograms, to preserve their spatial coherence, and is directly compatible with the Small BAseline Subset (SBAS) DInSAR technique. In particular, the algorithm investigates the differential phase gradient directly computed from the wrapped interferograms, and is focused on the estimation of the perpendicular baseline and of the parallel baseline azimuth rate components, separately performed along the range and azimuth directions, respectively. Starting from the estimations carried out on the interferograms, we then retrieve the orbit correction associated with each SAR acquisition of our time-series by solving a system of linear equations via the SVD method, extending the SBAS inversion concept also to the orbit estimation problem. Key application of this technique is the generation of deformation time-series from interferometric sequences of RADARSAT-1 SAR acquisitions which are available for several areas in the world, but are characterized by significantly low accuracy of the or bit information. The presented results, obtained by processing a data set consisting of 33 RADARSAT-1 images of Big Island at Hawaii, show that we may retrieve DInSAR time-series with sub centimeter accuracy, thus confirming the effectiveness of the pro posed technique.

We present an efficient space-time phase unwrapping (PhU) algorithm that allows us to process sequences of multitemporal full resolution differential synthetic aperture radar (SAR) interferograms for the generation of deformation time-series. The core of the proposed technique, dealing with sparse data grids, is represented by the extended minimum cost flow (MCF) (EMCF) PhU algorithm that was originally developed for the analysis of sequences of multilook interferograms. In particular, our method relies on the joint analysis of the spatial and temporal relationships among a set of properly selected multitemporal differential interferograms, which are compatible with the Small BAseline subset (SBAS) deformation time-series technique. The key point of the approach is the idea to split the complex MCF network problem, representing the overall PhU operation, into that of simpler subnetworks. More precisely, we start by identifying and solving a primary network that involves a proper selection of coherent pixels of the computed interferograms, representing the backbone structure of the overall network. Subsequently, this result is applied for constraining the solution of the subnetworks connected to the primary one, involving the entire set of analyzed pixels. To achieve this task, we solve a constrained optimization problem based on the computation of a constrained Delaunay triangulation in the azimuth/range domain. The overall procedure is implemented through two successive processing steps that are both carried out by using the EMCF PhU technique, which has been slightly modified to take into account the Doppler centroid differences of the exploited interferometric SAR data pairs. The experimental results, achieved by applying the proposed approach to a data set consisting of European Remote Sensing (ERS) SAR data acquired from June 1992 to August 2007 over the Napoli (Italy) bay area, confirm the effectiveness of the proposed PhU approach.

We present a simple approach to jointly exploit stripmap and ScanSAR acquisitions to generate differential synthetic aperture radar interferometry (DInSAR) time series. In particular, we extend the capability of the Small BAseline Subset (SBAS) approach to compute deformation time series from a set of stripmap images by filling possible temporal gaps in the available SAR data sequence with ScanSAR acquisitions. The starting point of our approach is the raw data focusing step, which is properly carried out to align the characteristics of the ScanSAR images to those of the stripmap ones. To achieve this task, we exploit stripmap processing codes to focus both SAR data types, the ScanSAR ones being processed on a burst-by-burst basis, accounting also for possible differences of the pulse repetition frequency with respect to that of the stripmap data. The coherent combination of the focused bursts generates phase-preserved ScanSAR images with the same output geometry and pixel spacing as the stripmap ones. This allows a straightforward implementation of the next steps of the SBAS processing chain, including the interferogram generation operation. In this case, we concentrate on a selection of small baseline (SB) stripmap–stripmap multilook interferograms identified through a Delaunay triangulation, which are complemented with a set of hybrid SB stripmap–ScanSAR interferograms. This interferogram selection permits us to develop an effective phase unwrapping algorithm based on a two-step processing strategy. Finally, the whole data set of unwrapped interferograms is inverted through the SBAS technique to retrieve the final deformation time series, including both stripmap and ScanSAR data. The proposed stripmap–ScanSAR SBAS processing approach is particularly attractive because it is very easy to implement since it requires only limited modifications with respect to the conventional stripmap-based SBAS algorithm. Our approach has been applied to descending and ascending hybrid stripmap–ScanSAR data sets of Envisat/ASAR C-band acquisitions from the Big Island of Hawaii. In spite of not including any common-band azimuthal filtering, which would account for the ScanSAR burst spectral properties at the expense of the algorithm simplicity, the presented results show that we may retrieve DInSAR time series with an accuracy ranging between 5 and 10 mm, consistent with previous C-band data analyses using only stripmap data.

In this work we extend the Small BAseline Subset (SBAS) Differential SAR Interferometry approach to allow the generation of deformation time-series by processing long sequences of ERS-1, ERS-2 and ENVISAT SAR data at the full spatial resolution scale. Our idea is to avoid the generation of ERS/ENVISAT cross-interferograms, severely affected by noise phenomena, and to consider single-platform interferograms only (i.e. ERS/ERS and ENVISAT/ENVISAT interferograms), that are properly combined by applying the SVD-based SBAS approach. The presented results, achieved on two dataset relevant to the Napoli Bay area and to the Murge region (Italy), confirm the effectiveness of the presented technique and demonstrate the relevance of deformation analysis carried out at the scale of single buildings with more than 15 years of ERS and ENVISAT acquisitions.

On April 6th2009 a Mw=6.3 earthquake struck the area around the city of L'Aquila in Italy. SAR systems have been proven to be valuable sensors for analyzing the effect of earthquakes and monitoring post-seismic displacements. Due to the low deformation rate, the study of post-seismic events requires the use of a multi-temporal InSAR approach. COSMO/SKYMED is a constellation of SAR sensors of 4 X-band sensors operative also for the civilian use. Thanks to the availability of a stack of ascending acquisitions, ad hoc programmed by ASI on the area stricken by the earthquake, it was possible to provide post-seismic deformation maps by using two different multi-temporal interferometric approaches: the SPINUA and SBAS techniques. The work is carried out in the framework of the MORFEO project dedicated to the monitoring of the landslides risk by means of Earth Observation data. The displacement maps related to the post-seismic activity are presented and commented. The results clearly show the potentiality of the COSMO/SKYMED constellation use for emergency monitoring.

We propose advances on the generation of deformation time series in areas affected by large deformation dynamics, where the exploitation of the differential SAR phase can be strongly limited by severe misregistration errors or by very high fringe rates. First, to overcome the former issue, we present an extension of the amplitude-based Pixel-Offset (PO) analyses by applying the Small BAseline Subset (SBAS) strategy, in order to move from the investigation of single (large) deformation events to that of dynamic phenomena. Secondly, to handle the high fringe rate interferograms, we subtract from them properly generated synthetic deformation models allowing us to reduce the fringe rate, thus helping the phase unwrapping step. The proposed approaches have been tested on ASAR-ENVISAT data acquired on Galápagos Islands and validated via continuous GPS measurements.

We perform a full exploitation of the Differential SAR Interferometry (DInSAR) algorithm referred to as Small BAseline Subset (SBAS) technique to investigate long term surface deformation occurring in extended, seismogenetic areas. To this aim we benefit of the SBAS technique capability to work in multi-frame and multi-sensor scenarios in order to improve the spatial and temporal coverage, as well as to employ new generation SAR sensors to increase the temporal sampling of the retrieved time series. In this work we apply the SBAS algorithm to analyze the temporal evolution of the detected displacements affecting three different seismogenetic scenarios by means of deformation time series retrieved through data acquired by European (ERS-1/2, ENVISAT) and Italian (COSMO-SkyMed) satellites. In particular, we focus on the analysis of the deformation patterns associated with the activity of the San Andreas (SAF, California, USA), the North Anatolian (NAF, Turkey) and the Paganica (PF, Abruzzo, Central Italy) Faults. The achieved results provide a clear idea of the surface deformation retrieval capability of the SBAS procedure.

We investigate the displacement phenomena affecting Mauna Loa and Kïlauea volcanoes at Big Island (Hawaii, USA), by applying an advanced ScanSAR-to-stripmap differential Synthetic Aperture Radar Interferometry (InSAR) approach. The implemented method, based on the application of the well-known Small BAseline Subset (SBAS) technique, allows the generation of LOS mean deformation velocity maps and corresponding time series, leading us to characterize the complex deformation of Mauna Loa and Kïlauea volcanoes. The presented analysis relies on the use of a SAR dataset composed by 49 ASAR ENVISAT satellite images, relevant to both stripmap and ScanSAR operational modes, acquired on descending orbits (track 200) from January 2003 to September 2008. Moreover, in order to assess the quality of the proposed combined ScanSAR-to-stripmap approach, we perform a comparison between the achieved DInSAR results and the LOS-projected GPS displacement measurements.

We extend the Small BAseline Subset (SBAS) algorithm to generate deformation time-series from SAR data acquired by the Canadian Space Agency (CSA) RADARSAT-1 sensor. The proposed approach is mostly oriented to the investigation of large scale deformation events with relatively low spatial resolutions (of about 100 × 100 m), and is based on the use of conventional multi-look interferograms with small temporal and spatial baseline separations. With respect to the original SBAS approach, several improvements are required to take into account the inaccuracies on the knowledge of the RADARSAT-1 orbital parameters, and of the significant fluctuations of the dop-pler centroid values over each single SAR scene. This work is aimed to present the first results achieved by applying the implemented RADARSAT-1 SBAS processing chain to an archive of SAR scenes, acquired in the time interval from 2000 to 2003 over, and relevant to the Hawaii Island area. The presented results markedly confirm the effectiveness of the implemented RADARSAT-1 SBAS processing chain.

We present the results of the first experiment to ¿plug¿ the Small BAseline Subset (SBAS) DInSAR algorithm into a GRID-based system; the key idea is to combine the robustness of the exploited advanced interferometric SAR approach with the high computing capability provided by a GRID environment. In particular, we have exploited the low-resolution SBAS algorithm and we benefited of the availability of the ESA Grid Processing-on-demand environment. The presented results, carried out on ENVISAT data, provide a overview of the main characteristics of the implemented SBAS-GRID processing solution.

We investigate the deformation of Mauna Loa and Kllauea volcanoes, Hawai'i, by exploiting the advanced differential Synthetic Aperture Radar Interferometry (InSAR) technique referred to as the Small BAseline Subset (SBAS) algorithm. In particular, we present time series of line-of-sight (LOS) displacements derived from SAR data acquired by the ASAR instrument, on board the ENVISAT satellite, from the ascending (track 93) and descending (track 429) orbits between 2003 and 2008. For each coherent pixel of the radar images we compute time-dependent surface displacements as well as the average LOS deformation rate. Our results quantify, in space and time, the complex deformation of Mauna Loa and Kllauea volcanoes. The derived InSAR measurements are compared to continuous GPS data to asses the quality of the SBAS-InSAR products.

We present the results of the first experiment to survey the temporal evolution of the deformation affecting very large areas using the small baseline subset (SBAS) differential synthetic aperture radar interferometry (DInSAR) algorithm. In particular, we have analyzed a set of 264 descending European Remote Sensing (ERS) SAR data frames from 1992 to 2000; these data are relevant to an area in central Nevada (U.S.) that extends for about 600times100 km. The starting point of our study has been the generation of an appropriate set of small baseline multilook interferograms computed from long SAR image strips, which were obtained by jointly focusing six contiguous raw data frames. Following their generation, the selected interferograms, which are computed on a spatial grid of 160times160 m, have been inverted via the SBAS technique to retrieve, for each coherent pixel, the displacement time series and the corresponding mean deformation velocity. The presented results are, to our knowledge, the first ones with such an extended multitemporal SAR data set, and they demonstrate the effectiveness of the approach to analyze the deformation of the investigated zone.

In this work we describe the application of the basic small BAseline subset (SBAS) technique, which exploits multilook interferograms, to a number of active volcanic areas. The use of such a technique reduces the amount of data to be processed and simplifies the analysis of geophysical phenomena occurring in extended areas (up to 100 km by 100 km). Moreover, it can be trivially extended in order to combine data acquired by different sensors with similar geometrical and electromagnetic characteristics, i.e., ERS and ENVISAT IS-2 mode, thus allowing an easy extension of the temporal observation window. The selected test sites for this study are Long Valley caldera, Mt. Etna and the Neapolitan Volcanic district (Mt. Vesuvio and Campi Flegrei caldera). Finally, we shortly analyze the implications of the use of forthcoming SAR sensors that operates in L-and X-bands.

We have investigated the deformation affecting the Campi Flegrei caldera (Italy), from 2002 to the end of 2006, by analyzing ENVISAT ASAR IS-2 data. This study has been performed by exploiting the SBAS-DInSAR algorithm that allows us to detect earth surface displacements and to investigate their temporal evolution via the generation of deformation time series. The presented analysis highlighted the renewed volcanic activity that started on mid-2005; moreover, we have combined the ascending and descending data in order to separate the vertical and east-west components of the detected displacements. The obtained results have been confirmed by the leveling data collected by the Osservatorio Vesuviano.

We have investigated the phase retrieval capability of the Extended Minimum Cost Flow (EMCF) phase unwrapping (PhU) approach in the framework of the Persistent Scatterers Interferometry (PSI) algorithms for the generation of deformation time-series. The presented technique exploits an appropriate selection of full resolution interferograms obtained via a triangulation step carried out in the Temporal/Perpendicular baseline plane, where the available SAR image sequence is represented. Moreover, the phase unwrapping procedure is implemented via the cascade of two steps, based on the conventional Minimum Cost Flow (MCF) algorithm, that allows us to exploit both the spatial characteristics and the temporal relationships among the produced interferograms. The presented experiments, carried out by analyzing a set of 43 ERS SAR images relevant to an area of the city of Rome (Italy), confirm the effectiveness of the exploited PhU approach.

In this paper, an extension of the minimum cost flow (MCF) algorithm dealing with a sparse data grid, which allows the unwrapping of multitemporal differential synthetic aperture radar (SAR) interferograms for the generation of deformation time series, is presented. The proposed approach exploits both the spatial characteristics and the temporal relationships among multiple interferograms relevant to a properly chosen sequence. In particular, the presented solution involves two main steps: first of all, for each arc connecting neighboring pixels on the interferometric azimuth/range grid, the unwrapped phase gradients are estimated via the MCF technique applied in the temporal/perpendicular baseline plane. Following this step, these estimates are used as a starting point for the spatial-unwrapping operation implemented again via the MCF approach but carried out in the azimuth/range plane. The presented results, achieved on simulated and real European Remote Sensing satellite SAR data, confirm the effectiveness of the extended MCF unwrapping algorithm

We exploit the small baseline subset (SBAS) algorithm for generating deformation time-series from SAR data acquired by sensors with different characteristics but with the same illumination geometry. In particular, our approach is focused on the use of European Remote Sensing (ERS) and ENVISAT satellite data, the latter acquired by the Advanced Synthetic Aperture Radar sensor on the IS2 swath. The proposed solution is oriented to investigate large-scale displacements with a relatively low spatial resolution (about 100/spl times/100 m) and implements an easy but effective combination of ERS and ENVISAT multilook interferograms which benefits of the temporal overlap between the acquisitions of the two sensors. Moreover, the algorithm does not rely on specific hypothesis on the spatial or temporal characteristics of the investigated deformations. Presented results, achieved on a synthetic aperture radar dataset relevant to the Napoli city area (Italy), confirm the validity of the approach.

The Small BAseline Subset (SBAS) approach is a differential synthetic aperture radar interferometry (DIFSAR) technique that allows analyzing deformation phenomena affecting both extended area and localized structures, by exploiting the phase difference of SAR image pairs characterized by small baselines. In this work we process a large set of ERS-1/2 data, acquired from ascending and descending orbits, at both small and large scale resolution and extend the obtained time series with available ENVISAT acquisitions. The results, relevant to the Napoli Bay area (Italy), are compared with geodetic and GPS data in order to achieve a quantitative analysis of the SABS algorithm performances

We present a differential synthetic aperture radar interferometry (DIFSAR) technique for generating displacement maps and deformation time-series by exploiting data acquired by the ERS and ENVISAT/ASAR systems. The proposed solution is focused on investigating large scale displacements with a relatively low spatial resolution (of about 100 /spl times/ 100 m). Since the two radar sensors operate at different carrier radar frequencies, the easy computation of cross-platform interferograms is prevented, thus leading to independent subsets of interferograms. An easy and efficient way to combine ERS/ERS and ENVISAT/ENVISAT multilook interferograms is represented by the application of the Small BAseline Subset (SBAS) strategy. This solution guarantees the continuity in the monitoring of the Earth's surface by benefiting of the present and future availability of ASAR data, preserving the archived ERS data. The presented results, obtained on a descending ERS and ENVISAT SAR data set, spanning the time interval from 1992 until October 2003, are relevant to the Napoli Bay area (Italy) and clearly demonstrate the effectiveness of the proposed extension of the SBAS technique.

In this work we present the results of an airborne differential interferometry experiment carried out by using the ORBISAR X-band system. Use of a high track measurement accuracy allowed us to fulfill the requirements of Fornaro et al. (2003), where repeat pass phase accuracy has been quantified in terms of position measurement errors. The presented experiment can be considered a first step towards the development of operative airborne differential interferometry for accurate ground deformation monitoring.

This paper presents a differential synthetic aperture radar (SAR) interferometry (DIFSAR) approach for investigating deformation phenomena on full-resolution DIFSAR interferograms. In particular, our algorithm extends the capability of the small-baseline subset (SBAS) technique that relies on small-baseline DIFSAR interferograms only and is mainly focused on investigating large-scale deformations with spatial resolutions of about 100/spl times/100 m. The proposed technique is implemented by using two different sets of data generated at low (multilook data) and full (single-look data) spatial resolution, respectively. The former is used to identify and estimate, via the conventional SBAS technique, large spatial scale deformation patterns, topographic errors in the available digital elevation model, and possible atmospheric phase artifacts; the latter allows us to detect, on the full-resolution residual phase components, structures highly coherent over time (buildings, rocks, lava, structures, etc.), as well as their height and displacements. In particular, the estimation of the temporal evolution of these local deformations is easily implemented by applying the singular value decomposition technique. The proposed algorithm has been tested with data acquired by the European Remote Sensing satellites relative to the Campania area (Italy) and validated by using geodetic measurements.

This paper presents a DIFSAR approach that allows us to detect and follow the temporal evolution of surface deformations at different spatial scales. In particular, our solution extends the capability of the algorithm referred to as small baseline subsets (SBAS) technique (Berardino et al., 2002), and allows us to investigate large scale deformation phenomena as well as displacements of single buildings or structures. The proposed technique relies on small baseline DIFSAR interferograms only, in order to mitigate the decorrelation phenomena, and requires two different sets of data generated at low (multi-look) and high (single-look) spatial resolution, respectively. The algorithm has been tested with the data acquired by the European Remote Sensing (ERS) satellites which are relative to the city of Napoli (Italy) and surroundings; the results have been validated by using geodetic data.

We propose a new approach for monitoring the evolution of Earth surface deformations via differential SAR interferometry. It allows us to overcome the limitations of standard techniques by incorporating the knowledge of the deformations associated to tie points and available spatial deformation models.

MINERVA (Monitoring by Interferometric SAR of Environmental Risk in Volcanic Areas) is a small scale service demonstration project financed by ESA in the Data User Programme framework. The objective of the project is the design, development and assessment of a demonstrative information service based on the interferometric processing of images from the ASAR instruments on board ERS1/2 and ENVISAT-I. The system is based on a new approach for the processing of INSAR data, which allows to optimize the quality of interferograms spanning from 35 days up to several years, and to merge them to generate a single solution describing the temporal evolution of the ground deformations in the examined risk area. The system allows to update this solution each time a new SAR image is available, and constitutes therefore an innovative tool for monitoring of the ground displacements in risk areas.

We present results obtained via an innovative spaceborne SAR interferometry algorithm showing that, despite its quiescent stage, the Somma-Vesuvius volcanic complex is subject to a particular deformation process. This is characterized by a rather continuous subsidence effect, revealed by both ERS satellite data and levelling surveys, between 1992 and 2000. These deformations are mainly localized in two zones involving the Vesuvius cone and a narrow discontinuous annular area that extends around the base of the Somma edifice. A likely explanation of subsidence at both sites involves the joint effect of gravitational sliding and extensional tectonic stress occurring at the contact between different lithological units.

In the area of tomographic synthetic aperture radar processing we present a new technique that makes use of the singular value decomposition method to improve the resolution limits by including a-priori information about the radiation penetration depth.

This paper presents a new solution for detecting and following the temporal evolution of small scale deformation phenomena; in particular our approach extends the capability of the SBAS technique, presented in P. Berardino et al. (2001), which is mainly focused on investigating large scale deformations with spatial resolutions of about 100 m/spl times/100 m. The proposed technique relies on small baseline differential SAR (DIFSAR) interferograms only, but it is implemented by using two different sets of data generated at low (multi-look data) and high spatial resolution (single-look data), respectively. The former are used to identify and estimate, via the SBAS technique or O. Mora et al. (2001, 2002), possible atmospheric phase artifacts and large scale deformation patterns; the latter to detect, on the high resolution residual phase components, structures highly coherent in time (buildings, rocks, lava structures, etc.), identified jointly to their heights and displacements. In particular the estimation of the temporal evolution of these local deformations is easily implemented by applying the SVD technique. The presented algorithm has been tested with data acquired by the European Remote Sensing (ERS) satellites relative to the Campania area (Italy).

We present a new differential synthetic aperture radar (SAR) interferometry algorithm for monitoring the temporal evolution of surface deformations. The presented technique is based on an appropriate combination of differential interferograms produced by data pairs characterized by a small orbital separation (baseline) in order to limit the spatial decorrelation phenomena. The application of the singular value decomposition method allows us to easily "link" independent SAR acquisition datasets, separated by large baselines, thus increasing the observation temporal sampling rate. The availability of both spatial and temporal information in the processed data is used to identify and filter out atmospheric phase artifacts. We present results obtained on the data acquired from 1992 to 2000 by the European Remote Sensing satellites and relative to the Campi Flegrei caldera and to the city of Naples, Italy, that demonstrate the capability of the proposed approach to follow the dynamics of the detected deformations.

The authors present a new spotlight SAR data-focusing algorithm based on a two-step processing strategy that combines the advantages of two commonly adopted processing approaches: the efficiency of SPECAN algorithms and the precision of stripmap focusing techniques. The first step of the proposed algorithm implements a linear and space-invariant azimuth filtering that is carried out via a deramping-based technique representing a simplified version of the SPECAN approach. This operation allows the authors to perform a bulk azimuth raw data compression and to achieve a pixel spacing smaller than (or equal to) the expected azimuth resolution of the fully focused image. Thus, the azimuth spectral folding phenomenon, typically affecting the spotlight data, is overcome, and the space-variant characteristics of the stripmap system transfer function are preserved. Accordingly, the residual and precise focusing of the SAR data is achieved by applying a conventional stripmap processing procedure requiring a minor modification and implemented in the frequency domain. The extension of the proposed technique to the case of high bandwidth transmitted chirp signals is also discussed. Experiments carried out on real and simulated data confirm the validity of the presented approach, which is mainly focused on spaceborne systems.

Efficient and precise compensation of the range cell migration (RCM) effect is a key point for a fast and accurate synthetic aperture radar (SAR) data processor. In particular the range-dependent nature of the range cell migration effect complicates the compensation operation. It has been recently shown that an exact compensation of the range-dependent RCM (RDRCM) phenomenon can be carried out either by applying the chirp scaling algorithm or the chirp z-transform procedure. This paper investigates the relationship between the two methods. In particular, it is shown that the chirp z-transform based approach represents a particular implementation of the chirp scaling algorithm. A final discussion is dedicated to show how the chirp z-transform and the chirp scaling procedure can be applied within a SAR data processing algorithm.

Any method that permits retrieving full range (unwrapped) phase values starting from their (-/spl pi/,/spl pi/) determination (wrapped phase) can be defined as a phase unwrapping technique. This paper addresses a new procedure for phase unwrapping especially designed for interferometric synthetic aperture radar applications. The proposed algorithm is based on use of Green's first identity. Results on simulated as well as on real data are presented. They both confirm the excellent performance of the procedure.

The authors exploit the interferometric multifrequency potentiality of the SIR-C/X-SAR system which is equipped with an L-, C-, and X-band sensor. They present a solution to improve the unwrapping performance of the C- and X-band data by considering the L-band unwrapped pattern. A new algorithm for the generation of a single digital elevation model (DEM) combining L-, C-, and X-band information is presented. This solution is based on the fusion of the unwrapped phase patterns by using a Kalman filter. The proposed fusion operation also accounts for the coherence characteristics of the three data sets. The selected test site is the Mt. Etna region in Italy which is very interesting from the volcanological and geological point of view. Numerical assessments of the achieved results are provided by evaluating the height accuracy with respect to a reference DEM.

Precision synthetic aperture radar (SAR) data processing requires the compensation of the range-dependent range cell migration (RDRCM) phenomenon. This paper describes a new method that permits the compensation of the RDRCM effect using a nonstandard Fourier transform (FT). This operation is applied, in the two-dimensional Fourier domain, to the range signal spectrum and allows the compensation of the azimuth frequency-dependent scaling factor due to the RDRCM. The nonstandard FT is performed via a chirp z-transform that is carried out with one convolution operation and two phase multiplications.>

Repeat-pass interferometry data were acquired during the first and second SIR-C/X-SAR missions in April and October 1994. This paper presents the first results from X-SAR interferometry at four different sites. The temporal separations were one day and six months. At two sites the coherence requirements were met, resulting in high quality interferograms. A digital elevation model in ground range geometry has been derived. The limitations of the X-SAR interferometry are discussed.>

The authors explore the determination of motion errors of an aircraft platform from the signum-coded synthetic-aperture-radar raw signal (SC-SAR data). The SC-SAR has a one-bit representation. In fact it only provides information about the sign of SAR recorded data. Displacement from the line-of-sight (LOS) direction and nominal forward velocity of SAR platform are estimated from this signal by using the reflectivity displacement method (RDM), which allows motion error determination directly from SAR raw data. The theory of the motion error estimation from the signum-coded raw data is presented and supported by experimental results. Comparison is made between the motion parameters computed by the original and the SC-data in order to show that the motion errors can be also determined by applying the RDM to the SC-signal.>