This study briefly overviews the methodologies employed at the mining pilot site in Austria, to demonstrate continuous monitoring of raw material extraction (progress, stability, waste dumps) under the scope of S34I project. Several unmanned aerial vehicle (UAV) flights were performed, and tri-stereo digital elevation models (DEMs) were computed and compared to estimate the volume of the material extracted and deposited. The low-cost global navigation satellite system (GNSS)-based monitoring system was installed in April 2023 and provided near real-time data. In addition, state-of-the-art Interferometric synthetic aperture radar (InSAR) processing provided dense displacement times series in 3D to create the deformation model.

This study investigates the potential of a statistical-based, adaptive approach to unwrapping sequences of differential synthetic aperture radar (SAR) interferograms that cover a large swath of the terrain. The proposed method adopts a multiscale decomposition strategy to identify efficiently and then process sets of coherent points at different spatial scales. The coherent point selection process is performed considering the statistical properties of the stack of wrapped multilooked SAR interferograms generated at various scales. Overall, the adopted procedure allows automatically recognizing the areas in large swath interferograms where significant and reliable phase changes occur while moving from neighboring spatial scales. Over these regions, multiscale phase unwrapping (PhU) operations are performed efficiently, with a computational improvement and without losing significant information. To this aim, the implementation of a conditioned space-time PhU scheme that operates sequentially at different spatial grids is detailed. Then, the unwrapped interferograms are inverted to generate ground displacement time series through advanced multitemporal interferometric SAR (MT-InSAR) approaches, recovering information at different scales (from local to regional/continental). Experimental results have been obtained by applying the developed scheme to large-swath SAR datasets collected at the C band by Sentinel-1 sensors. The results demonstrate the feasibility and soundness of the developed multiscale PhU method.

This study investigates the efficacy of incoherent and coherent SAR descriptors for filling spatial and temporal gaps in optical-driven Leaf Area Index (LAI) time series. Within this context, an artificial intelligence (AI) algorithm based on Multi-Output Gaussian Process (MOGP) [1], [2] demonstrated its effectiveness in handling the different information derived from SAR signatures in a unified corpus. The study utilizes sequences of Sentinel-2 imagery to derive Leaf Area Index (LAI) maps, while Sentinel-1 observations over the same area are utilized to obtain SAR backscatter coefficients and interferometric coherence data. This comprehensive dataset is then employed as input for training the MOGP model. Experimental tests demonstrate the usefulness of the MOGP model in obtaining accurate LAI time series even during very cloudy periods.

In this work, a Change Detection (CD) analysis was conducted based on the assessment of ground deformation (subsidence) in the Venice Lagoon over recent years. The multi-temporal interferometric Small Baseline Subset (SBAS) technique [1], [2] was employed to analyze the interconnections between the subsidence in the area and the occurrence of extreme flood events, with a specific focus on the floods that occurred in November 2019. Examining the time series of backscattered signals from the Sentinel-1 (S-1) synthetic aperture radar (SAR) sensor, we identified the extent of the flooded regions and evaluated the impact of the floods on the city. The potential of a newly developed Artificial Intelligence (AI) method [3] based on Random Forest [4], [5] was exploited. This methodology leverages the capability of several coherent/incoherent SAR change detection indices (CDIs) and their mutual interaction in a single corpus for rapid mapping of surface changes. This method [3] has shown great success in rapidly mapping land surface changes of areas in Sardinia and Sicily that were affected by large wildfires in the summer of 2021 and flooded areas in Houston and GalvestonBay as a result of Hurricane Harvey in 2017. In conclusion, the comprehensive CD/SBAS analysis provided valuable insights into the relationship between subsidence and recent extreme flood events in the Venice Lagoon, revealing the dynamics of the lagoon and its vulnerability to such events.

This study briefly overviews the methodologies employed for generating ground displacement time series of areas subject to severe phenomena, emphasizing the azimuthal (i.e., about north-south) components not seen from conventional interferometric SAR analyses. To face this problem efficiently, multiple aperture interferometry (MAI) and, more recently, burst overlapped interferometry (BOI) have been proposed. Here, the authors of this investigation would like to provide the readers with some experiments conducted in heterogeneous contexts to demonstrate the validity of BOI but also to point out its evident limitations (in terms of the coverage areas and the expected precision of the ground measurements) and shade lights on potential further developments of such techniques. The presented results refer to two selected regions, i.e., the Galapagos Island and the Ridgecrest (U.S.) earthquake area.

In the last two decades, satellite Multi-Temporal Differential Interferometric SAR (DInSAR) techniques have become consolidated tools for studying slow-moving landslides. Moreover, the availability of sensors using different band frequencies and the development of advanced processing algorithms have constantly improved. In this work we discuss the first results of an experimental analysis carried out on multi-temporal multi-baseline L-Band data acquired by the newly launched SAOCOM satellites in the framework of the MEFISTO project founded by the Italian Space Agency (ASI), one of the main aims of which was to test the potentiality of multi-frequency (X, C and L bands) DInSAR data processed with different methodologies applied to a multi-scale slow-landslide analysis.

In this study, we show the potential of a synergistic use of COSMO-SkyMed (CSK) and Sentinel-1 (S-1) synthetic aperture radar (SAR) data for the analysis of ground deformations in coastal regions potentially subjected to flood risk. The global climate change and the inherent increase of the mean sea level exacerbate the risk. In this work, we want to focus on the capability of high-resolution CSK images, combined with medium-resolution Sentinel-1 data, to jointly provide a more comprehensive view of the changes occurring in highly urbanized coastal regions, such as the one represented by the megacity of Shanghai. Specifically, we have applied a split-spectrum method to identify a set of highly coherent SAR pixels. The location and spatial density of such permanent (and the semi-permanent scatters) have revealed helpful to generate a flood risk map for the coastal area of Shanghai in a worst-case scenario. To this aim, we have used the LISFLOOD-FP hydrodynamic model to forecast possible inundation patterns and used the estimated density of high-coherent SAR pixels into the inundated areas to quantify the coastal flood impacts. Similar analyses could also be extended to other coastal regions in the World, as the West Coast of U.S.

Recent investigations have shown that multi-look (ML) SAR interferograms with very short baselines are seriously affected by undesired short-lived phase artefacts, which can negatively influence the reliability of the InSAR products (e.g., ground displacement time-series, mean displacement velocity maps). In this work, we present a strategy to estimate and mitigate such phase bias effects, by making exclusive use of ML InSAR phase triplets. Specifically, we present a stationary (time-invariant) method that keeps the simplification that the InSAR phase bias signal in an ML interferogram exclusively depends on the temporal baseline of the considered interferogram, and not on the specific acquisition times of the interfering SAR images. The results demonstrate the validity of the proposed method using both simulated and real SAR data.

Among the several applications of multipass InSAR, monitoring of landslides represents one of the most challenging case because of the topographic characteristics, the heterogeneous displacement rates and the frequent presence of vegetation. In this study, we consider the use of multi-frequency InSAR data for the application to case studies located in the North and South Italy.

This work proposes a multigrid differential synthetic aperture radar (SAR) interferometry (InSAR) technique for the detection of ground displacements at different spatial scales. The method relies on efficient phase-unwrapping (PhU) operations performed at the native spatial scales. In particular, a set of multi-look interferograms are first unwrapped using conventional (or advanced) PhU algorithms at the regional scale. Subsequently, ML unwrapped interferograms are used to facilitate the PhU operations performed at the local scale (single-look). Specifically, the unwrapped multi-look interferograms are resampled to the single-look grid and modulo-$2\pi $subtracted to the single-look interferograms. These phase residuals are then unwrapped and added back to the multi-look resampled interferograms. To accomplish these operations, at variance with alternative multiscale methods, no (linear/nonlinear) models are used to fit the spatial high-pass phase residuals. Finally, the unwrapped single-look interferograms are properly inverted to retrieve the ground displacement time series using any small baseline (SB)-oriented multitemporal InSAR tool. Experimental results are performed by processing a set of SAR data acquired by the X-band COSMO-SkyMed sensor over the coastal area of Shanghai, China.

This work explores the properties characterizing the phase non-closure of multi-look synthetic aperture radar (SAR) interferograms. Specifically, we study the implications of multi-look phase time incongruences on the generation of ground displacement time-series through small baseline (SB) multi-temporal InSAR (Mt-InSAR) methods. Our research clarifies how these phase inconsistencies can propagate through a time-redundant network of SB interferograms and contribute, along with phase unwrapping (PhU) errors, to the quality of the generated ground displacement products. Moreover, we analyze the effects of short-lived phase bias signals that could happen in sequences of short baseline (SB) interferograms and propose a strategy for their mitigation. The developed methods have been tested using both simulated and real SAR data. The latter were collected by the Sentinel-1A/B (C-band) sensors over the study areas of Nevada state, U.S., and Sicily Island, Italy.

This work proposes a method for estimating and compensating the atmospheric phase screen (APS) in sets of synthetic aperture radar (SAR) interferograms generated with a ground-based SAR (GB-SAR) instrument. We address the presented approach’s physical, statistical, and mathematical framework by discussing its potential and limitations. In contrast with other existing algorithms that estimate the APS from the unwrapped phase signals, our methodology is based on the straightforward analysis of the wrapped phases, directly. Therefore, the method is not affected by any potential phase unwrapping mistake, and it is suitable for multitemporal interferometric SAR (InSAR) applications. The effects of the local topography, the decorrelation noise, and the ground deformation on the APS estimates are deeply studied. Experiments performed on simulated and real GB-SAR InSAR data corroborate the validity of the theory. In particular, the simulated results show that the method is beneficial in zones with medium-to-high topographic slopes (e.g., for Alpine and mountainous regions).

Tropospheric excess path delays afflict surface motion measurements when Ground-based SAR (GB-SAR) interferometry is adopted. In this work, we propose an adaptive frequency-domain methodology to estimate the atmospheric phase screen (APS) components using wrapped GB-SAR interferometric data pairs, thus avoiding any possible phase unwrapping mistake. The experimental results show that the developed technique can detect and compensate for tropospheric fluctuations found in steep mountain areas without using any external digital elevation model of the investigated area. The paper addresses the potential of the developed technique by applying it on a set of simulated data.

In this work, a novel approach for the correction of misregistration errors in sequences of Terrain Observation with Progressive Scan (TOPS) Sentinel-1 SAR data is presented. The method represents a further evolution of the Enhanced Spectral Diversity (ESD) approaches. Remarkably, the developed algorithm is almost insensitive to the presence of large azimuth ground displacements due, for instance, to massive earthquakes, volcanic eruptions or glacier movements. Indeed, in such non-stationary contexts, the conventional ESD and network ESD approaches for the SAR TOPS data co-registration reveals problematic being co-registration errors and azimuth ground deformation components mixed out. Preliminary experiments conducted on a set of TOP SAR data related to the area hit by the Ridgecrest earthquake MW 7.1, California, on July 04 2019 confirm the validity of the theoretical framework.

In this article, we present an innovative space–time adaptive multilooking technique that operates on a sequence of multitemporal, differential synthetic aperture radar interferograms. The developed approach relies on the application of the fundamentals of directional statistics theory. At variance with other methods that identify the set of statistically homogenous pixels (SHPs) within a multilooking (complex averaging) window based on the statistics of the single-look-complex (SLC) SAR images, the proposed method is exclusively based on the analysis of the multitemporal sequence of full resolution DInSAR interferograms. The SHPs are then used to generate spatially adaptive multilooked interferograms both at the native, full-scale grid of the SLC images and at the multilooked resolution scale. The algorithm is effective and simple to implement, only requiring the availability of a sequence of full-scale differential SAR interferometry (DInSAR) interferograms. The interferograms can then be used to generate ground displacement time-series through advanced multitemporal interferometric SAR (MTInSAR) approaches. Experimental results obtained by applying the adopted technique to two SAR data sets acquired at X- and L-band, respectively, demonstrate the validity of the developed method.

In this work, the theoretical foundations of a method to identify and routinely correct the phase unwrapping errors affecting a sequence of time-redundant sequence of differential SAR (DInSAR) interferograms are presented and discussed. Although the proposed method has principally been developed to work with differential SAR interferograms that form a triangle-shaped network in the temporal/perpendicular baseline plane, as originally exploited by Extended Minimum Cost Flow (EMF) phase unwrapping (PhU) technique, it may simply be adapted to work with any network of time-redundant interferograms. The fundamental idea is to exploit the phase inconsistencies of triples of (already) unwrapped sequence of interferograms, prior to the application of any inversion for the generation of surface displacement time-series, to independently identify, for every SAR pixels, the 2π-multiple integers corrections to be applied to the unwrapped phases for adjusting the time-inconsistent phase unwrapping errors. This goal is achieved by solving a simple and computationally efficient network-based minimum-cost-flow optimization problem. Some preliminarily experimental results, which have been obtained by processing a sequence of Cosmo-SkyMed (CSK) SAR images collected over the California bay area, are presented.

This work is based on the use of weighted Least-squares (WLS) approaches for the generation of ground line-of-sight (LOS) displacement time-series through differential interferometric SAR methods. More precisely, in this paper, a WLS method that extends the usability of the Multi-Temporal InSAR (MT-InSAR) Small Baseline Subset (SBAS) algorithm in regions with medium-to-low coherence is presented. The proposed method relies on the adaptive selection and exploitation, pixel-by-pixel, of the medium-to-high coherent interferograms, such as to discard the noisy phase measurements. In such a way, for each pixel, it is possible to obtain several disjoined sets of interferograms, which are then connected by exploiting the weighted singular value decomposition (WSVD) method. As a consequence, the interferogram networks reduction may lead to rejecting some SAR acquisitions from the used dataset, this results in the generation of so called variable-length displacement time-series. The experimental results have been performed considering by applying the Weighted Adaptative Variable-lEngth (WAVE) technique to a dataset composed of 50 SAR images, gathered by the Italian Space Agency Cosmo-SkyMed (CSK) constellation sensors over the Basilicata region, Southern Italy.

In this study, we show the potential of an adaptive quad-tree-based decomposition method applied to Differential Synthetic Aperture Radar (DInSAR) data. Specifically, the proposed method exploits a multi-resolution scheme for the phase unwrapping of sequences of DInSAR interferograms and allows one to produce DInSAR deformation products at different scales of resolution. The selection of the used multi-grid is based on the analysis of the statistical properties of a sequence of interferometric phase, allowing to recognizing major deformation areas where phase unwrapping operations can be performed more efficiently, with a computational improvement and without losing significant information.

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.

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.

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.

In this work, we investigate the evolution of ground deformation in the Shanghai coastal region, China, over the last decade. We performed a Small BAseline subset DInSAR analysis based on the exploitation of three independent SAR datasets acquired by the ASAR/ENVISAT (ENV), COSMO-SkyMed (CSK), and Sentinel-1A (SlA) sensors, spanning the time frame between 2007 and 2017. More precisely, we used a time-dependent geotechnical centrifuge model of the subsidence signals to combine the available time-gapped CSK and ENV datasets, and hence we produced a long-term CSK, ENV, and SlA displacement time-series. The maximum subsidence rate calculated by the combined long-term ENV+CSK+S1A deformation time-series is about 30 mm/year. The achieved results are beneficial for future studies assessing the environmental risk (e.g., due to flooding) of the population living in the coastal area of Shanghai.

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, a new method for the noise filtering and the adaptive multi-looking of a sequence of multi-temporal differential SAR interferograms, which relies on the use of directional (circular) statistics, is presented. At variance with other similar approaches, which identify homogenous distributed scatterers (DS) in a resolution cell by analyzing the statistics of the complex-valued single-look-complex (SLC) SAR images, our method is exclusively based on the analysis of statistics of the phase interferograms, thus making no other assumptions. The developed technique can be applied to generate adaptive multi-look interferograms both at the native grid of full-resolution images and/or at the multi-look resolution scale. The preliminary experimental results, achieved by applying the proposed approach to a dataset consisting in 93 SAR data acquired by the ERS-1/2 radar sensors from 1992 to 2008 over the Gulf of Napoli and its surrounding area (South Italy) confirm the effectiveness of the proposed method.

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.

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.

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.

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.

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.

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.

In this work, we investigate the temporal evolution of the ground deformation over the ocean-reclaimed area of the Shanghai megacity (China). To this aim, we performed an integrated SBAS-DInSAR analysis by benefiting from the availability of SAR data collected at C-band by the ENVISAT ASAR sensor, and at X-band by the COSMO-SkyMed and TerraSAR-X SAR constellations, spanning the overall time interval between 2007 and 2015. The joint exploitation of different SAR datasets and the knowledge of time-dependent models for the expected subsidence in ocean-reclaimed platforms is helpful to retrieve long-term displacement time series, thus allowing us to get new insights about the future evolution of the reclamation area settlements over the next years.

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.

Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR) coherence, a measurement of the similarity between two radar acquisitions, is a widely used index to quantitatively evaluate the quality of derived interferograms considering its direct relationship with Signal to Noise Ratio (SNR). When interferometrie measurements originate from both point-like and distributed targets, the coherence has to be correctly estimated by adaptively grouping and averaging pixels sharing similar scattering properties. In this paper, we present a method to estimate coherence for DInSAR stack by exploiting the multi-temporal phase statistics. The key point of the proposed solution relies on the capability of averaging each pixel with its statistical homogeneous neighborhood, thus allowing a more accurate coherence estimation. Experimental results on real SAR data acquired by ENVISAT satellite, over Shanghai city, China demonstrate the validity of proposed method.

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.

In this work we investigate the deformation signals affecting the coastal region of the megacity of Shangai (China) where, to satisfy the growing land demand for industrial and urban development, man-made lands reclaimed from the sea have been retrieved and used to build airports, harbors, and industrial areas. The presented analysis is carried out through the application of spaceborne Differential Synthetic Aperture Radar (SAR) Interferometry (DInSAR) techniques. A general picture of the ground deformation field is provided by applying the Small Baseline Subset (SBAS) as well as the Permanent Scattereres (PS) approaches to archives of SAR images collected by ENVISAT and COSMO-SkyMed sensors from 2007 to 2014 over the investigated area.

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 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 paper the analysis of time series of fractal dimension maps generated from multi-pass SAR images is dealt with. The objective is twofold: on the one hand such an analysis is addressed to assess the performance of the algorithm for the fractal dimension estimation from a single SAR image, i.e., to verify the fractal estimation efficiency on natural scenes within the multiple sensor passes. On the other hand, by exploiting the statistics evaluated starting from the fractal time series, a final fractal dimension map, including only the areas in which the fractal estimation is efficient and strongly denoised from the speckle effect, is obtained. The analysis has been performed on a Cosmo-SkyMed data-set of 42 stripmap images spanning the time period from October 2009 to December 2012, acquired over the Somma-Vesuvius volcanic complex (South Italy), which is in a quiescent stage since the last eruption occurred in 1944.

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.

In this paper the analysis of fractal dimension maps extracted from Cosmo-SkyMed amplitude-only SAR data is considered. The focus is on the Somma-Vesuvius volcanic complex, whose study allows to draw significant comments on the behavior of the fractal maps. In particular, the dependence of the fractal maps on geometrical and geomorphologic characteristics of the imaged surface is investigated. Preliminary results regarding the different behavior of the fractal dimension evaluated on the Gran Cono and on the Mt. Somma are presented.

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.

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.

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.

In this letter, we investigate the potential of the small baseline subset (SBAS) differential synthetic aperture radar interferometry (DInSAR) technique to produce consistent deformation time series by using data sets of SAR images with high Doppler centroid (DC) frequencies. To cope with this issue properly, we exploited an archive of SAR scenes acquired by the European Remote Sensing 2 (ERS-2) sensor after the February 2000 three-gyroscope navigation mode failure. Our approach is oriented toward the long-term investigation of large-scale displacements with low spatial resolution (about 100 × 100 m) by processing sets of SAR images without discarding scenes depending on their DC values. Our analysis involves a set of descending SAR data frames from 1992 to 2007 relevant to the Napoli (Italy) bay area. Comparison with contemporaneous Global Positioning System measurements clearly confirms the effectiveness of the proposed approach.

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.

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 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.

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.

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.

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.