SAR Datasets Research Articles

Statistically homogeneous pixel selection for small SAR data sets based on the similarity test of the covariance matrix

ABSTRACTThe selection of statistically homogeneous pixels (SHPs) has a great significance in interferometric applications. The essence of the measurement of statistical homogeneity lies in the assessment of the degree of similarity between two pixels. Various amplitude-based strategies have been proposed for selecting SHPs. However, the detection rates of these methods are usually unsatisfactory in the case of small data sets. To overcome this limitation, in this work, SHPs are selected based on the similarity test of the covariance matrix. In addition, the covariance matrix of each pixel is estimated by an adaptive M-estimator, which has a robust performance in heterogeneous scenes. In the implementation process, image segmentation is introduced to guide the selection of SHPs in each search window. The feasibility and effectiveness of the proposed method are demonstrated using experiments on simulated and real data.

LANDSLIDE MONITORING USING INSAR TIME-SERIES AND GPS OBSERVATIONS, CASE STUDY: SHABKOLA LANDSLIDE IN NORTHERN IRAN

Abstract. Shabkola is a village located in Mazandaran province of northern Iran that suffers from the mass movement happening in the upstream. Deforestation and changes to land use are the main reasons for the soil instability in this region, which together with steep slope, relatively high precipitation rate and natural erosion has led to such a condition. The area of mass movement is approximately 90 hectares which is a big threat for people living in the region. In this study, we have utilized two different geodetic techniques including InSAR time-series analysis and GPS measurements to assess slope stability in Shabkola. The SAR dataset includes 19 ALOS/PALSAR images spanning from July 2007 to February 2011 while GPS observations are collected in 5 campaigns from September 2011 to May 2014. Displacement as much as approximately 11.7 m in slope direction was detected by GPS observations for the 2011-2014 time period. Most of the slope geometry is in north-south direction, for which the sensitivity of InSAR for displacement detection is low. However, ALOS PALSAR data analysis revealed a previously unknown landslide, covered by dense vegetation in the northern part of main Shabkola landslide, showing line-of-sight velocity of approximately 2cm/year in the time period 2007-2011.

The 1996 Mw 6.6 Lijiang earthquake: Application of JERS-1 SAR interferometry on a typical normal-faulting event in the northwestern Yunnan rift zone, SW China

The northwestern Yunnan rift zone in the Yunnan Province of China is a seismically active region located along the western boundary of the Sichuan–Yunnan Block on the eastern margin of the Qinghai–Tibetan Plateau. An earthquake with a magnitude of 6.6 (Mw) occurred in this region on February 3, 1996. The Lijiang earthquake was the largest normal-faulting event to occur along the western boundary of the Sichuan–Yunnan Block in the last 40years. In this study, we used L-band JERS-1 (Japanese Earth Resources Satellite-1) SAR data sets from two descending orbits to detect surface deformation signals surrounding the epicentral region in order to estimate the source parameters through an inversion of the displacement fields. The results indicated that the earthquake can be explained by slip along two segments of the ∼N–S trending listric normal fault, named the Lijiang–Daju fault. Coseismic deformation patterns and slip distributions revealed that the earthquake consisted of two sub-events, which were also suggested by seismological results. Based on an analysis of the static Coulomb stress change, the second sub-event was likely triggered by the first sub-event. The central segment of the Lijiang–Daju fault, which has an eastward-convex geometry, did not rupture during the earthquake. This phenomenon was probably related to a geometrical discontinuity at the fault-bend area of the Lijiang–Daju fault.

Separating volcanic deformation and atmospheric signals at Mount St. Helens using Persistent Scatterer InSAR

Over the past two decades, GPS and leveling surveys have recorded cycles of inflation and deflation associated with dome building eruptions at Mount St. Helens. Due to spatial and temporal limitations of the data, it remains unknown whether any deformation occurred prior to the most recent eruption of 2004, information which could help anticipate future eruptions. Interferometric Synthetic Aperture Radar (InSAR), which boasts fine spatial resolution over large areas, has the potential to resolve pre-eruptive deformation that may have occurred, but eluded detection by campaign GPS surveys because it was localized to the edifice or crater. Traditional InSAR methods are challenging to apply in the Cascades volcanic arc because of a combination of environmental factors, and past attempts to observe deformation at Mount St. Helens were unable to make reliable observations in the crater or on much of the edifice.In this study, Persistent Scatterer InSAR, known to mitigate issues of decorrelation caused by environmental factors, is applied to four SAR data sets in an attempt to resolve localized sources of deformation on the volcano between 1995 and 2010. Many interferograms are strongly influenced by phase delay from atmospheric water vapor and require correction, evidenced by a correlation between phase and topography. To assess the bias imposed by the atmosphere, we perform sensitivity tests on a suite of atmospheric correction techniques, including several that rely on the correlation of phase delay to elevation, and explore approaches that directly estimate phase delay using the ERA-Interim and NARR climate reanalysis data sets. We find that different correction methods produce velocities on the edifice of Mount St. Helens that differ by up to 1cm/yr due to variability in how atmospheric artifacts are treated in individual interferograms. Additionally, simple phase-based techniques run the risk of minimizing any surface deformation signals that may themselves be correlated with elevation. The atmospherically corrected PS InSAR results for data sets overlapping in time are inconsistent with one another, and do not provide conclusive evidence for any pre-eruptive deformation at a broad scale or localized to the crater or edifice leading up to the 2004 eruption. However, we cannot rule out the possibility of deformation less than ~1cm/yr, or discern whether deformation rates increased in the months preceding the eruption. The results do significantly improve the spatial density of observations and our ability to resolve or rule out models for a potential deformation source for the pre-eruptive period.

An approach for accurately retrieving the vertical deformation component from two-track InSAR measurements

ABSTRACTAs is well known, both conventional differential synthetic aperture radar interferometry (D-InSAR) and multi-temporal synthetic aperture radar interferometry (MT-InSAR) have a common limitation that they only can measure the deformation component along the radar line of sight (LOS) direction. However, in the majority of disaster investigations, there is more interest in the vertical deformation component than that of the horizontal direction, for example, in measuring ground subsidence in urban areas, or ground subsidence due to underground mining. To estimate the vertical deformation component accurately, it is in theory necessary to create at least three independent equations, and solve the vertical, the North–South, and the East–West deformation components by exploiting at least three-track InSAR LOS measurements or combining at least two-track InSAR LOS measurements with azimuth measurements. However, these methods are greatly limited and sometimes not even practical because there is typically little chance of obtaining a three-track SAR data set covering the same area in the same time span, and the accuracy of the azimuth measurements is far lower than that of InSAR LOS measurements. In this article, we found that it is possible to solve for the vertical deformation component from two-track InSAR LOS measurements in some circumstances. Then, an approach for accurately retrieving the vertical deformation component from two-track InSAR LOS measurements is proposed, and the analytical expression is presented. The approach is illustrated through an investigation of the ground subsidence in an area of Beijing, China. Unlike previous methods, this approach can accurately retrieve the vertical deformation component from two-track InSAR LOS measurements, and provide more reliable results for improving the interpretation of ground subsidence phenomena.

Synthetic Aperture Radar Ship Detection Using Haar-Like Features

The detection of ships at sea is a complex task made more so by adverse weather conditions, lack of night visibility and large areas of concern. Synthetic Aperture Radar imagery with large swaths can provide the needed coverage at a reduced resolution. The development of ship detection methods that can effectively detect ships despite the reduced image resolution is an important area of research. A novel ship detection method is introduced that makes use of a standard Constant False Alarm Rate prescreening step followed by a cascade classifier ship discriminator. Ships are identified using Haar-like features using AdaBoost training on the classifier with an accuracy of 89.38% and false alarm rate of 1.47 x 10-8 across a large swath Sentinel-1 and RADARSAT-2 newly created SAR dataset.

Polarimetric decomposition for the selection of persistent scatterers

ABSTRACTIn this paper, the issue of employing polarimetric processing methods for persistent scatterers (PS) selection is addressed. The scope of the paper regards the processing methods for PS generation by means of coherent target decomposition (CTD). The key is to exploit scatterer physical characteristics, besides scatterer coherence and stability, so as to classify them as PS utilizing as less data sets as possible. Cameron’s CTD is proposed for PS classification based on scatterers physical properties as an alternative to polarization phase difference (PPD) for it can eliminate the scatterer initial orientation angle. Additionally, provides a complete set of elemental scattering mechanisms in order to describe the physical properties of the scatterer, and thus create a more reliable PS net. These assets make Cameron’s CTD outperform PPD in exploiting scatterer physical properties for PS selection. The study also focuses on minimizing the number of used data sets for PS net generation, which led to experimenting with only two fully polarimetric SAR data sets. The results are promising enough to establish the proposed method as an auxiliary tool to be utilized prior to any classic interferometric techniques for early scene evaluation in order to decide whether the scene would be fruitful for applying permanent scatterer interferometry or not.

A Novel Technique for Analysis of Ice Properties using GABOR Filtering

Objectives: The SAR images are highly noisy so a technique is applied that can de-noise the input image for batter analysis of the features. Methods: RADARSAT1 imagery data is utilized by Synthetic Aperture Radar to detect the ice at different regions of the seas. Automated algorithm gives better consequence of target utilizing R1 imagery data. To de-noise the input satellite images, gobar filter is applied. The gobar filter is the patch based filter in which patches are created of the whole image and patch which has dissimilar properties are removed from the image. Finding: The proposed and existing algorithms are implemented in MATLAB by considering the SAR dataset. It is analyzed that after de-noising the image, properties of the image like thickness, concentration and velocity is analyzed efficiently. The results of the image such as ice thickness, ice velocity and ice concentration is increased up to 10 %, 12 % and 18 %, respectively.

Tsunami Detection using Remote Sensing Technique and Calculation of Fault Parameters: Brief Review

Objective: The main objective of this work is to provide and over view of the tsunami detection using remote sensing technique and to calculate the earthquake fault parameters such as fault length, width, area and displacements. Method: Tsunami early warning systems have provided to be the extreme importance after the tsunami that hit Japan in March 2011. This research article presents a case study based on the tsunami detection using Bottom Pressure Rate (BPR) measurement and the post the analysis using the SAR datasets. A final decision based system using BPR has been studied to carry out the measurements of tsunami wave parameters. SAR based study has also been carried out for the post tsunami studies. Wiener filters are utilized to remove the speckle noise presents in imagery. Future scope of this work has also been proposed. The study and analysis for the tsunami because which is generally due to the seabed displacements have been carried out in this research article. With the certain profiling of the underwater geological movements, the Tsunami generation occurs in deep and near-shore Ocean. Findings: The generation and propagation of N-waves occurs underneath and propagates from deep to near-shore without the deformation processes. Since the magnitude and length of the tsunami waves are very high and moves closer to the solitary waves which represents the tsunami in a better manner. The result shows the variation of the displacement from 11cm to about 1995cm at the magnitudes variation from 6.0 to 9.5. It means there is impact of 172% as compared to the magnitude of 6.0 at which the bottom pressure dissemination is minimum. Application/Improvements: The recent researches are to predict the early warning using satellite remote sensing techniques. This study may provide an overview to detect the tsunami signals using radar/remote sensing technologies.

Moving Target Detection via Efficient ATI-GoDec Approach for Multichannel SAR System

Clutter suppression and ground moving target indication (GMTI) are challenging tasks for multichannel synthetic aperture radar (MC-SAR) systems. In recent years, robust principal component analysis (RPCA), such as the augmented Lagrange multiplier method (ALM) and go decomposition (GoDec) algorithm, has drawn considerable attention for its excellent performance in distinguishing the different parts from a set of correlative database. In this letter, an efficient along-track interferometry GoDec (ATI-GoDec) approach is proposed for GMTI in MC-SAR systems under a strong clutter background. The proposed method can be separated into two sections: the predetection and the postdetection. The predetection by an efficient ATI RPCA method can decrease missing targets, and postdetection with a novel magnitude and phase (M&P) detection has the ability to reduce the false targets. As a result, the proposed method can provide a more robust performance by a comparison to the traditional ATI detection. It can also widen the tolerant values of the preset cardinality and decrease the probability of false alarm compared with the conventional GoDec algorithm. Moreover, the proposed method only takes several iterations to reach the convergence by solving the optimization problem of the RPCA model, which makes it more efficient than the previous RPCA methods. The results by applying the proposed method into a set of real SAR data are consistent with the analysis presented in this letter.

Land Cover Classification in SubArctic Regions Using Fully Polarimetric RADARSAT-2 Data

The expansion of shrub vegetation in Arctic and sub-Arctic environments observed in the past decades can have significant effects on northern ecosystems. There is a need for efficient tools to monitor those changes, not only in terms of the spatial coverage of shrubs, but also their vertical growth. The objective of the current paper is to evaluate the performance of polarimetric C-band SAR datasets for land cover classification in sub-Arctic environments. A series of RADARSAT-2 quad-pol images were acquired between October 2011 and April 2012. The Support Vector Machine (SVM) classification scheme was used on three sets of features: the elements of the polarimetric coherency matrix [ T ] , the parameters extracted from a polarimetric decomposition based on the eigenvalues and eigenvectors of [ T ] and the parameters extracted from a model-based decomposition. Using a single image, the results show that the best classification accuracies ( ≈ 75 % ) are obtained using the [ T ] matrix with the October images. When adding a second image to the feature set, either from two different dates or two incidence angles, the classification accuracy is improved and reaches 90 . 1 % with two images from October 2011 and April 2012 at 27 ∘ incidence. The results show that C-band polarimetric SAR imagery is an adequate tool to map shrub vegetation in sub-Arctic environments.

Ground Subsidence in the Beijing-Tianjin-Hebei Region from 1992 to 2014 Revealed by Multiple SAR Stacks

The coordinated development of the Beijing-Tianjin-Hebei has become a national strategy with Beijing and Tianjin as twin engines driving the regional development. However, the Beijing-Tianjin-Hebei region has suffered dramatic ground subsidence during last two to three decades, mainly due to long-term groundwater withdrawal. Although, annual spirit leveling has been conducted routinely in some parts of Beijing and Tianjin, and InSAR technique has also been used to monitor ground subsidence in some local areas of the region, there is a lack of a complete survey of ground subsidence over the whole region. In this paper, we report a research on mapping ground subsidence in the Beijing-Tianjin-Hebei region over a long time span from 1992 to 2014. Three SAR datasets from four satellites are used: ERS-1/2 SAR images from 1992 to 2000, ENVISAT ASAR images from 2003 to 2010, and RADARSAT-2 images from 2012 to 2014. An improved multi-temporal InSAR method, namely “Multiple-master Coherent Target Small-Baseline InSAR” (MCTSB-InSAR), has been developed to process the datasets. A unique feature of MCTSB-InSAR is the adjustment process useful for wide area monitoring which provides an integrated solution for both calibration of InSAR-derived deformation and the harmonization of the deformation estimates from overlapping SAR frames. Three maps of the subsidence rate corresponding to the three periods over the wide Beijing-Tianjin-Hebei region are generated, with respective accuracy of 8.7 mm/year (1992–2000), 4.7 mm/year (2003–2010), and 5.4 mm/year (2012–2014) validated by more than 120 leveling measurements. The spatial-temporal characteristics of the development of ground subsidence in Beijing and Tianjin are analyzed. This research represents a first-ever effort on mapping ground subsidence over very large area and over long time span in China. The result is of significance to serve the decision-making on ground subsidence mitigation in the Beijing-Tianjin-Hebei region.

MONITORING OF LAND SUBSIDENCE IN RAVENNA MUNICIPALITY USING INTEGRATED SAR - GPS TECHNIQUES: DESCRIPTION AND FIRST RESULTS

The Emilia Romagna Region (N-E Italy) and in particular the Adriatic Sea coastline of Ravenna, is affected by a noticeable subsidence that started in the 1950s, when the exploitation of on and off-shore methane reservoirs began, along with the pumping of groundwater for industrial uses. In such area the current subsidence rate, even if lower than in the past, reaches the -2 cm/y. Over the years, local Authorities have monitored this phenomenon with different techniques: spirit levelling, GPS surveys and, more recently, Differential Interferometric Synthetic Aperture Radar (DInSAR) techniques, confirming the critical situation of land subsidence risk. In this work, we present the comparison between the results obtained with DInSAR and GPS techniques applied to the study of the land subsidence in the Ravenna territory. With regard to the DInSAR, the Small Baseline Subset (SBAS) and the Coherent Pixel Technique (CPT) techniques have been used. Different SAR datasets have been exploited: ERS-1/2, ENVISAT, TerraSAR-X and Sentinel-1. Some GPS campaigns have been also carried out in a subsidence prone area. 3D vertices have been selected very close to existing persistent scatterers in order to link the GPS measurement results to the SAR ones. GPS data were processed into the International reference system and the comparisons between the coordinates, for the first 6 months of the monitoring, provided results with the same trend of the DInSAR data, even if inside the precision of the method.

MONITORING OF LAND SUBSIDENCE IN RAVENNA MUNICIPALITY USING INTEGRATED SAR - GPS TECHNIQUES: DESCRIPTION AND FIRST RESULTS

Abstract. The Emilia Romagna Region (N-E Italy) and in particular the Adriatic Sea coastline of Ravenna, is affected by a noticeable subsidence that started in the 1950s, when the exploitation of on and off-shore methane reservoirs began, along with the pumping of groundwater for industrial uses. In such area the current subsidence rate, even if lower than in the past, reaches the -2 cm/y. Over the years, local Authorities have monitored this phenomenon with different techniques: spirit levelling, GPS surveys and, more recently, Differential Interferometric Synthetic Aperture Radar (DInSAR) techniques, confirming the critical situation of land subsidence risk. In this work, we present the comparison between the results obtained with DInSAR and GPS techniques applied to the study of the land subsidence in the Ravenna territory. With regard to the DInSAR, the Small Baseline Subset (SBAS) and the Coherent Pixel Technique (CPT) techniques have been used. Different SAR datasets have been exploited: ERS-1/2, ENVISAT, TerraSAR-X and Sentinel-1. Some GPS campaigns have been also carried out in a subsidence prone area. 3D vertices have been selected very close to existing persistent scatterers in order to link the GPS measurement results to the SAR ones. GPS data were processed into the International reference system and the comparisons between the coordinates, for the first 6 months of the monitoring, provided results with the same trend of the DInSAR data, even if inside the precision of the method.

On the joint exploitation of long-term DInSAR time series and geological information for the investigation of ground settlements in the town of Roma (Italy)

Abstract In this work the long-term DInSAR deformation time series have been jointly exploited with the geological information and the structural characteristics of the buildings, in order to study the interactions between the soils and the man-made structures and to investigate the response of different buildings to the settlements. The presented analysis has been carried out by applying the advanced multi-temporal DInSAR technique referred to as Small BAseline Subset (SBAS) approach to an ERS/ENVISAT SAR dataset, collected over the town of Roma (Italy) in the 1992–2010 time interval. Our study has been performed first at the large spatial scale, and then at the local scale, focusing on some buildings located within the city centre on the eastern side of the Tevere River. The obtained SBAS-DInSAR measurements, analysed in light of the geological setting and the spatial distribution of the settlements, indicate that the observed displacements are mainly linked to a residual consolidation process, correlated with the urbanization and hydrometric level of the Tevere River. The local variation in the deformation rates can be related to the intrinsic variability of the soils filling the alluvial valley and to time since loading application.

Spatial Estimation of Classification Accuracy Using Indicator Kriging with an Image-Derived Ambiguity Index

Traditional classification accuracy assessments based on summary statistics from a confusion matrix furnish a global (location invariant) view of classification accuracy. To estimate the spatial distribution of classification accuracy, a geostatistical integration approach is presented in this paper. Indicator kriging with local means is combined with logistic regression to integrate an image-derived ambiguity index with classification accuracy values at reference data locations. As for the ambiguity measure, a novel discrimination capability index (DCI) is defined from per class posteriori probabilities and then calibrated via logistic regression to derive soft probabilities. Integration of indicator-coded reference data with soft probabilities is finally carried out for mapping classification accuracy. It is demonstrated via a case study involving classification of multi-temporal and multi-sensor SAR datasets, that the proposed approach can provide a map of locally-varying accuracy values, while respecting the overall accuracy derived from the confusion matrix. It can also highlight areas where the benefit of data fusion was significant. It is expected that the indicator approach presented in this paper could be a useful methodology for assessing the spatial quality of classification results in a probabilistic way.

Reconstructing the Vertical Component of Ground Deformation from Ascending ALOS and Descending ENVISAT Datasets—A Case Study in the Cangzhou Area of China

. Due to the regular revisit periods of SAR platforms and the decorrelation of SAR images, the estimation of ground deformation retrieved from a particular SAR dataset may be of low resolution in both spatial and temporal dimensions. To enhance the spatial and temporal sampling rates in ground deformation estimation, a multiplatform SAR dataset combination strategy is proposed, based on the Multi-Temporal InSAR (MTInSAR) technique and the Same Name target point Pairs (SNPs) combination approach. The combination strategy has been tested and applied in the Cangzhou area, which has suffered from ground subsidence due mainly to groundwater extraction. The derived results show that the deformation monitoring sampling rates can be improved in the space and time dimensions when compared to the results obtained from a single-platform SAR dataset, which makes the multiplatform SAR dataset combination strategy useful for uneven (spatially variable) and nonlinear (temporally variable) deformation detection. Moreover, the results derived from the combination strategy tend to be more reliable and have fewer noise points.

Comparison of Various Polarimetric Decomposition Techniques for Crop Classification

Differential response of crops to polarimetric radar signals provides the basis for crop discrimination, classification and monitoring. Polarimetric decomposition approaches provide a measure of the relative contributions of backscatter from different scattering mechanisms and hence, the selection of proper decomposition method plays a vital role in the classification of natural distributed targets especially in crop discrimination. The study focused towards assessing the performance of both coherent and incoherent decomposition methods in terms of discrimination of different cover types, mainly the crops like paddy and cotton using their classification accuracies. Decomposition techniques viz., Freeman, van Zyl, Yamaguchi-3 component and Krogager were applied on three Radarsat-2 fully polarimetric SAR data sets (Fine Quad mode) during the crop growth period. The decomposition parameters along with their temporal dynamics were studied over paddy and cotton crops. Temporal dynamics and the field observations revealed that the manifestation of paddy and cotton crops was best in second date of acquisition (26-Oct.-2010), coinciding with the peak vegetative stage of paddy crop and vegetative stage of cotton crop. It is observed that the scattering mechanisms occurring in paddy and cotton crops are distinct. Supervised parallelepiped minimum distance to mean classification was employed to classify the decomposed data. The analysis showed that Krogager and van Zyl gave best class accuracies as well as overall kappa accuracies (K^ of Krogager = 0.7265 and K^ of van Zyl = 0.6804). The crop type viz. paddy and cotton could be better discriminated and mapped from the classification of van Zyl and Krogager decompositions.

From linked open data to molecular interaction: studying selectivity trends for ligands of the human serotonin and dopamine transporter.

Retrieval of congeneric and consistent SAR data sets for protein targets of interest is still a laborious task to do if no appropriate in-house data set is available. However, combining integrated open data sources (such as the Open PHACTS Discovery Platform) with workflow tools now offers the possibility of querying across multiple domains and tailoring the search to the given research question. Starting from two phylogenetically related protein targets of interest (the human serotonin and dopamine transporters), the whole chemical compound space was explored by implementing a scaffold-based clustering of compounds possessing biological measurements for both targets. In addition, potential hERG blocking liabilities were included. The workflow allowed studying the selectivity trends of scaffold series, identifying potentially harmful compound series, and performing SAR, docking studies and molecular dynamics (MD) simulations for a consistent data set of 56 cathinones. This delivered useful insights into driving determinants for hDAT selectivity over hSERT. With respect to the scaffold-based analyses it should be noted that the cathinone data set could be retrieved only when Murcko scaffold analyses were combined with similarity searches such as a common substructure search.

Cloud Computing for Earth Surface Deformation Analysis via Spaceborne Radar Imaging: A Case Study

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.