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

In this paper, an advanced technique for the generation of deformation maps using synthetic aperture radar (SAR) data is presented. The algorithm estimates the linear and nonlinear components of the displacement, the error of the digital elevation model (DEM) used to cancel the topographic terms, and the atmospheric artifacts from a reduced set of low spatial resolution interferograms. The pixel candidates are selected from those presenting a good coherence level in the whole set of interferograms and the resulting nonuniform mesh tessellated with the Delauney triangulation to establish connections among them. The linear component of movement and DEM error are estimated adjusting a linear model to the data only on the connections. Later on, this information, once unwrapped to retrieve the absolute values, is used to calculate the nonlinear component of movement and atmospheric artifacts with alternate filtering techniques in both the temporal and spatial domains. The method presents high flexibility with respect to the required number of images and the baselines length. However, better results are obtained with large datasets of short baseline interferograms. The technique has been tested with European Remote Sensing SAR data from an area of Catalonia (Spain) and validated with on-field precise leveling measurements.

In this paper a methodology is proposed to elaborate landslide activity maps through the use of PS (Persistent Scatterer) data. This is illustrated through the case study of Tramuntana Range in the island of Majorca (Spain), where ALOS (Advanced Land Observing Satellite) images have been processed through a Persistent Scatterer Interferometry (PSI) technique during the period of 2007–2010. The landslide activity map provides, for every monitored landslide, an assessment of the PS visibility according to the relief, land use, and satellite acquisition parameters. Landslide displacement measurements are projected along the steepest slope, in order to compare landslide velocities with different slope orientations. Additionally, a ground motion activity map is also generated, based on active PS clusters not included within any known landslide phenomenon, but even moving, potentially referred to unmapped landslides or triggered by other kinds of geomorphological processes. In the Tramuntana range, 42 landslides were identified as active, four as being potential to produce moderate damage, intersecting the road Ma-10, which represents the most important road of the island and, thus, the main element at risk. In order to attest the reliability of measured displacements to represent landslide dynamics, a confidence degree evaluation is proposed. In this test site, seven landslides exhibit a high confidence degree, medium for 93 of them, and low for 51. A low confidence degree was also attributed to 615 detected active clusters with a potential to cause moderate damage, as their mechanism of the triggering cause is unknown. From this total amount, 18 of them intersect the Ma-10, representing further potentially hazardous areas. The outcomes of this work reveal the usefulness of landslide activity maps for environmental planning activities, being exportable to other radar data and different geomorphological settings.

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

In recent years, different deep learning techniques were applied to segment aerial and satellite images. Nevertheless, state of the art techniques for land cover segmentation does not provide accurate results to be used in real applications. This is a problem faced by institutions and companies that want to replace time-consuming and exhausting human work with AI technology. In this work, we propose a method that combines deep learning with a human-in-the-loop strategy to achieve expert-level results at a low cost. We use a neural network to segment the images. In parallel, another network is used to measure uncertainty for predicted pixels. Finally, we combine these neural networks with a human-in-the-loop approach to produce correct predictions as if developed by human photointerpreters. Applying this methodology shows that we can increase the accuracy of land cover segmentation tasks while decreasing human intervention.

Settlement and angular distortion affecting the construction can cause damages on structures and they could compromise the serviceability of the structure. This work focuses on the monitoring of a bridge located on the south part of the Valencia harbor that shows some opening joints and depressions in the pavement of both transition areas between the abutments and the embankments. For this purpose Advanced Differential Interferometry is used to exploit Synthetic Aperture Radar images (SAR) acquired by ALOS and Cosmo-SkyMed satellite sensors from February 2007 to July 2010 and from May 2010 to August 2012, respectively. This approach permitted to estimate surficial displacements, to identify areas of bridge affected by settlements and angular distortion and to relate them with the observed damages.This project was developed in the framework of the DORIS project (ground deformation risk scenarios: an advanced assessment service) funded by the EC-GMES-FP7 initiative (grant agreement 423 no. 242212) and by the Spanish Ministry of Economy and Competitiveness (MINECO) and EU FEDER, under project TIN2014-55413- C2-2-P. ALOS PALSAR images were provided by the project JAXA- 1209. The authors thank the University of Florence for founding Matteo Del Soldato for the PhD abroad period of research to the Department of Civil Engineering of the University of Alicante and the Autoridad Portuaria de Valencia for its kindly support