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This paper presents an unsupervised flood monitoring approach developed by DARES TECHNOLOGY using Synthetic Aperture Radar (SAR) space-borne images in order to improve disaster management and coordinate response activities in front of flood crisis events. The methodology developed combines SAR pre-processing, histogram thresholding in blocks with bi-modal behavior and unsupervised segmentation approaches. The use of interferometric parameters, such as the coherence is also employed to refine results. The challenge of this detection approach is working towards an unsupervised approach, i.e., training data is no required and, therefore, information or ground-truth about the class statistics within the area of interest are not necessary. The methodology proposed will be validated over the floods occurred in Mumbai city on August 29, 2017 produced by heavy rain events. 4 Sentinel-1 SAR images will be employed for this purpose.

This work addresses the suitability of using X-band Synthetic Aperture Radar (SAR) data for operational geotechnical monitoring of site scale slow moving landslides, affecting urban areas and infrastructures. The scale of these studies requires high resolution data. We propose a procedure for the practical use of SAR data in geotechnical landslides campaigns, that includes an appropriate dataset selection taking into account the scenario characteristics, a visibility analysis, and considerations when comparing advanced differential SAR interferometry (A-DInSAR) results with other monitoring techniques. We have determined that Sentinel-2 satellite optical images are suited for performing high resolution land cover classifications, which results in the achievement of qualitative visibility maps. We also concluded that A-DInSAR is a very powerful and versatile tool for detailed scale landslide monitoring, although in combination with other instrumentation techniques., This work was financially supported by the Spanish Ministerio de Economía y Competitividad (MINECO) research projects IPT-2011-1234-310000, ESP2013-47780-C2-1-R, and RTC-2014-1922-5. Research by RI and JJM has been also supported by the Big Risk project (contract number BIA2008-06614) and by the Spanish Ministry of Economy, Industry and Competitiveness (MINECO), the State Research Agency (AEI) and the European Funds for Regional Development (EFRD) under project TEC2017-85244-C2-1-P. CommSensLab is Unidad de Excelencia Maria de Maeztu MDM-2016-0600 financed by the Agencia Estatal de Investigación, Spain

Prior to the application of any persistent scatterer interferometry (PSI) technique for the monitoring of terrain displacement phenomena, an adequate pixel selection must be carried out in order to prevent the inclusion of noisy pixels in the processing. The rationale is to detect the so-called persistent scatterers, which are characterized by preserving their phase quality along the multi-temporal set of synthetic aperture radar (SAR) images available. Two criteria are mainly available for the estimation of pixels' phase quality, i.e., the coherence stability and the amplitude dispersion or permanent scatterers (PS) approach. The coherence stability method allows an accurate estimation of the phase statistics, even when a reduced number of SAR acquisitions is available. Unfortunately, it requires the multi-looking of data during the coherence estimation, leading to a spatial resolution loss in the final results. In contrast, the PS approach works at full-resolution, but it demands a larger number of SAR images to be reliable, typically more than 20. There is hence a clear limitation when a full-resolution PSI processing is to be carried out and the number of acquisitions available is small. In this context, a novel pixel selection method based on exploiting the spectral properties of point-like scatterers, referred to as temporal sublook coherence (TSC), has been recently proposed. This paper seeks to demonstrate the advantages of employing PSI techniques by means of TSC on both orbital and ground-based SAR (GB-SAR) data when the number of images available is small (10 images in the work presented). The displacement maps retrieved through the proposed technique are compared, in terms of pixel density and phase quality, with traditional criteria. Two X-band datasets composed of 10 sliding spotlight TerraSAR-X images and 10 GB-SAR images, respectively, over the landslide of El Forn de Canillo (Andorran Pyrenees), are employed for this study. For both datasets, the TSC technique has showed an excellent performance compared with traditional techniques, achieving up to a four-fold increase in the number of persistent scatters detected, compared with the coherence stability approach, and a similar density compared with the PS approach, but free of outliers.

In this paper, a study of polarimetric optimization techniques in the frame of differential synthetic aperture radar (SAR) interferometry (DInSAR) is considered. Historically, DInSAR techniques have been limited to the single-polarimetric case, mainly due to the unavailability of fully polarimetric data. Lately, the launch of satellites with polarimetric capabilities, such as the Advanced Land Observing Satellite (ALOS), RADARSAT-2, or TerraSAR-X, allowed merging polarimetric and interferometric techniques to improve the pixels' phase quality and, thus, the density and the reliability of the final DInSAR results. The relationship between the polarimetrically optimized coherence or amplitude dispersion maps and the final DInSAR results is carefully analyzed, using both orbital and ground-based SAR fully polarimetric data. DInSAR processing using polarimetric optimization techniques in the pixel selection process is compared with the classical single-polarimetric approach, achieving up to a threefold increase of the number of pixel candidates in the coherence case and up to a factor of seven in the amplitude dispersion case.

©2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Urban subsidence and landslides are among the greatest hazards for people and infrastructure safety and they require an especial attention to reduce their associated risks. In this framework, ground-based synthetic aperture radar (SAR) interferometry (GB-InSAR) represents a cost-effective solution for the precise monitoring of displacements. This work presents the application of GB-InSAR techniques, particularly with the RiskSAR sensor and its processing chain developed by the Remote Sensing Laboratory (RSLab) of the Universitat Politecnica de Catalunya (UPC), for the monitoring of two different types of ground displacement. An example of urban subsidence monitoring over the village of Sallent, northeastern of Spain, and an example of landslide monitoring in El Forn de Canillo, located in the Andorran Pyrenees, are presented. In this framework, the key processing particularities for each case are deeply analyzed and discussed. The linear displacement maps and time series for both scenarios are showed and compared with in-field data. For the study, fully polarimetric data acquired at X-band with a zero-baseline configuration are employed in both scenarios. The displacement results obtained demonstrate the capabilities of GB-SAR sensors for the precise monitoring of ground displacement phenomena.

The reliability of any final differential synthetic aperture radar (SAR) interferometry (DInSAR) product is directly related to the phase quality of the interferograms involved in the processing. Performing an appropriate selection of high-quality pixels is hence mandatory in order to avoid the inclusion of noisy data in the processing and thus obtain reliable deformation information within the area of study. In this framework, a new full-resolution pixel selection method based on exploiting the spectral properties of pointlike scatterers is presented. The method is developed from the concept of the so-called coherent scatterers, which are characterized by having a correlated spectrum in a single acquisition, but now involving the temporal axis in the detection. Using the spectral properties of scatterers to perform the DInSAR pixel selection step presents some advantages with respect to the well-known permanent scatterer (PS) approach. On the one hand, the radiometric calibration of data is not required since the detection is now not dependent on the amplitude behavior of scatterers. On the other hand, a reliable pixel selection can be performed even with a reduced number of images due to the nature of the estimator. In contrast, its main limitation relies on the loss of range resolution involved in the sublook generation process. The method is more suited for urban scenarios where the density of temporally stable pointlike scatterers is generally higher. In this context, the final deformation maps obtained using the new method presented are compared, in terms of density and phase quality, with the ones obtained with the well-known traditional PS approach, showing a similar performance. Finally, since both methods characterize pointlike scatterers from different points of view, the combination of both techniques is proposed leading to a significant increase in the pixels' density and giving place to a meaningful improvement in the final DInSAR results.

In this paper, a new model-based technique for the compensation of severe height-dependent atmospheric artifacts, using ground-based synthetic aperture radar (SAR) data over mountainous regions, is proposed. The method presented represents an extension of already existing techniques, but now taking into account the effect of steep topography in the atmospheric phase screen compensation process. In addition, the technique is adapted to work with polarimetric SAR data, showing, in that case, a noticeable improvement in the compensation process. The method is validated in the mountainous environment of El Forn de Canillo, located in the Andorran Pyrenees, where there is a slow-moving landslide that nowadays is being reactivated coinciding with strong rain episodes. In this framework, ten zero-baseline fully polarimetric data sets have been acquired at X-band during a one-year measurement campaign (October 2010-October 2011) with the GB-SAR sensor developed at the Universitat Politecnica de Catalunya. First, the impact of the severe atmospheric fluctuations among multitemporal GB-SAR measurements is carefully studied and analyzed. Hence, the need to correctly estimate and compensate the resulting phase differences when retrieving interferometric information is put forward in the frame of differential-SAR-interferometry applications.

Synthetic aperture radar (SAR) systems are inherently band limited in both range and azimuth, and hence, the point spread function (PSF) has the shape of a bidimensional sinc function. In addition, all SAR images are slightly oversampled, and as a consequence, the contribution of a single target extends to more than a single cell. The main lobe and the side lobes of strong scatterers are sometimes clearly visible in the images. This characteristic of the SAR images must be considered when applying differential interferometric synthetic aperture radar (DInSAR) pixel selection algorithms. For persistent scatterers, the properties, for instance, the amplitude stability, are preserved in both redundant information around the main lobe and side lobes. For this reason, a cluster of pixels rather than just the pixel position corresponding to the exact location of the target will be detected. Spatially variant apodization (SVA) is a nonlinear filter based on cosine-on-pedestal weighting functions able to achieve a total side-lobe cancelation without degrading the original image resolution. When working with complex data under complex scattering scenarios, the PSF moves away from the ideal bidimensional sinc, and the SVA performance worsens. The amplitude and phase of the original images could be distorted by the SVA filtering compromising the pixel selection and the quality of the final DInSAR results. In this letter, SVA is used to method locate in the image the side lobes of high-power scatterers and generate a mask while preserving the amplitude and phase of the original images.

This paper aims to demonstrate that radar-based remote sensing techniques can be as effective as the conventional geotechnical ones for the detection and monitoring of well suited areas. Many of the high mountain landslides are vegetated areas that decorrelate faster at X-band. As in these scenarios the number of coherent scatterers is low and, in addition, the area of interest is usually small, the processing can be benefited of the usage of high-resolution data. This will maximize the chances of detecting persistent scatters coming from both natural targets and man-made structures. The high resolution Spotlight mode of TerraSAR-X is thus the perfect choice as it offers a fine resolution. On the other hand, its 11 days of revisit time and X-band carrier allows the monitoring of small variations in the landslide trend and deal with its variable dynamics.

In this paper, the study of polarimetric optimization techniques for Differential SAR Interferometry (DInSAR) applications is analyzed. This work has been carried out in the framework of deformation map retrieval on landslides. A large number of landslides occur on vegetated areas with a poor density of temporal coherent scatterers, which are characterized by a fast decorrelation at X-band. The objective of the techniques proposed in this paper is to increase the number of temporal coherent scatterers to improve the robustness of the DInSAR algorithms exploiting the polarimetric capabilities of data. The relationship between optimum coherences and its corresponding phase quality in terms of DInSAR application is analyzed using Ground-Based SAR zerobaseline fully-polarimetric data.

Orbital Differential SAR Interferometry (DInSAR) is a well-known technique to retrieve terrain deformation phenomena from wide areas with high resolution. Historically its application has been limited to single polarization SAR, mainly due to the unavailability of polarimetric data. Lately, the launch of several satellites with polarimetric capabilities, such as Radarsat-2 or TerraSAR-X, allows merging polarimetric and interferometric techniques in order to improve the results obtained in the DInSAR processing. This work will explore the existent analytical techniques in order to optimize the quality of the subsidence results. The dataset used contains 35 Fine Quad-Pol Radarsat-2 acquisitions over the city of Barcelona (Spain).

“©2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. Ground-based synthetic aperture radar (SAR) (GB-SAR) sensors represent an effective solution for the monitoring of ground displacement episodes. Initially, the most GB-SAR sensors were based on vector network analyzers (VNA). This type of solution, characterized by a slow scanning time comparable to the decorrelation of the troposphere medium, compromised in many cases the quality of final products for the application of persistent scatterer interferomerty (PSI) techniques. The development of GB-SAR sensors based on the use of stepped linear frequency modulated continuous wave (SLFMCW) signals has led to significant improvements during the last years. They have allowed fulfilling the need of temporal homogeneity of the troposphere during the acquisition time and, moreover, they have favored the acquisition of reliable polarimetric SAR (PolSAR) measurements without drastically increasing the scanning time. This fact has boosted the inclusion of polarimetric SAR interferometry (PolInSAR) algorithms in PSI processing chains, which are demonstrating to outperform classical single-polarimetric performances. The objective of this paper is twofold. On the one hand, a general overview of the polarimetric RiskSAR sensor, developed by the Universitat Politecnica de Catalunya (UPC), is put forward as an example of SLFMCW GB-SAR system implementation. On the other hand, a complete theoretical description of ground-based SAR (GB-SAR) interferometry (GB-InSAR) techniques for PSI purposes is widely discussed. The adaptation of the Coherent Pixels Technique to obtain the linear and nonlinear components of ground displacement phenomena is proposed. In the second part of this paper, the displacement maps and time series over two very different scenarios are presented in order to show the feasibility of GB-SAR sensors for terrain displacement monitoring applications.

The availability of new Sentinel-1A/B (S1A/B) satellites provides geohazards community with a large amount of Synthetic Aperture Radar (SAR) acquisitions over critical areas around the world. The current S1A mission has a minimum re-visit time of 12 days, but with the recent launch of S1B, this temporal gap will be reduced to only 6 days. This high acquisition frequency, jointly with the large coverage and high quality of S1 data, allows the rapid generation of interferograms1,2 for sudden events, such as earthquakes. This rapid response is crucial for the damage evaluation and fault model refinement. This paper presents results obtained for the latest earthquakes occurred in Japan in April 2016. Two S1A interferograms in ascending and descending orbits have been generated, and after phase unwrapping3,4 processing the Line Of Sight (LOS) motion maps are obtained. This LOS1,2 component corresponds to the range distance between the satellite and ground, and it does not provide real horizontal and vertical information of the displacement. The usage of two different view directions, in ascending and descending orbits, allows the decomposition of LOS motion into horizontal (West-East) and vertical (Up-Down) directions. These new motion maps are essential to better understand the dynamics of the earthquake and to perform further analysis and modelling by seismic experts.

This paper presents the application of Differential Synthetic Aperture Radar (SAR) interferometry (DInSAR) algorithms for the precise monitoring of earth dams ground deformation. The test site selected for this study is the Conza Dam, located in the southern Apennines (Italy), very close to the epicenter of the big earthquake (M-w = 6.9) which took place on November 23th, 1980, striking the Irpinia region. Among the multiple advanced DInSAR techniques developed by the SAR community during the last decade, this work uses the so-called SUBSOFT software, developed by the Remote Sensing Laboratory (RSLab) group from the Universitat Politecnica de Catalunya (UPC), which is based on the use of Coherent Pixels Technique (CPT) algorithm. The analysis is carried out using 51 ENVIronmental SATellite-Advanced Synthetic Aperture Radar (ENVISAT-ASAR) images, corresponding to the period from 29th of November 2002 to the 30th of July 2010. In this framework, ground displacements recorded by a network of conventional ground-based sensors are also available for the same temporal span. Indeed, the embankment dam is well instrumented to measure internal settlements by means of cross-arms placed in six different cross-sections, and superficial displacements by means of targets for precise leveling. A statistical analysis has been performed to carry out a better comparison between the measurements obtained with the conventional field sensors and the interferometric data. The high agreement between final DInSAR displacements and in-situ instrumental data, demonstrates the reliability of such technique for the precise monitoring of civil infrastructures, and concretely, in dams with a high exposure factor and its consequent risk.