Currently, there are efforts to classify and predict future displacement events (e.g., landslides, infrastructure collapse) using machine learning from PSInSAR-derived deformation maps. This paper proposes two workflows for creating databases with information about disasters from scanned documents and online news sources. The databases can be used as ground truth to confirm that the deformation profiles from the training datasets are related to actual events or as input in future machine learning algorithms.

In this work, a synthetic aperture radar imaging system is evaluated. The acquisition scenario involves a Pulson P440 UWB-radar and a localization system based on stereo-cameras. The main goal of the sensing system is to image areas behind walls. However, since the SAR acquisition is performed by a human operator and considering the positioning errors, our focus is to compensate for these errors by developing an autofocus SAR procedure. Emergency and military are the envisaged applications. The technique proposed in this paper relies on a strong point scatterer and it is validated on both simulated and real-world data.

Kepler Space Inc. is creating a wide area Canadian Ground Motion Service (C-GMS), initially based on time series (every 6/12 days, between 2015 and 2022) of Sentinel-1 (S-1) satellite radar data and a modified Persistent Scatterers Interferometry (PSInSAR) approach to detect and map ground motion over time, with mm/year accuracy over wide areas (up to 700 km swaths). The information available through C-GMS follows the general format of the European Ground Motion Service, but with additional layers depicting seasonal motion, coherent after/before date, changes in displacement rates and break events. Presently C-GMS covers the Windsor-Quebec City corridor (roughly 1100 km in the East-West direction and around 250-500 km in the NS direction and is publicly available for viewing and data download at http://c-gms.com/.

Inspection of temporal deformation profiles from the Luțca bridge collapse, in Romania, showed that before the disaster, some PSI deformation profiles exhibits a pattern of subsidence/uplift followed by loss of coherence. In this work, it is proposed a new methodology for detecting such profiles, based on the removal of the seasonal component and piecewise linear modeling of deformation profiles. The methodology is not limited to the aforementioned types of profiles. The method is validated on an area including the Luțca bridge.

A remote operated C-band SAR sensor for agriculture applications was developed and deployed in the field. The sensor operates at a central frequency of 5.400 GHz. It comprises a 2D scanning system and monolbi-static channels which allow the operation in single- and repeatpass interferometry modes. Sensor is designed and developed as a ground-based configuration, simulating the measurement geometry of Sentinel-1 and a potential companion satellite, intended to provide single-pass interferometric products for 3D imaging applied on agricultural crops, in order to retrieve the biophysical parameters of plants and their development status.

In this paper are presented target displacement measurements using synthetic aperture radar interferometry, with the Pulson P440 radar module. The synthetic aperture is formed by automatically moving the radar with the help of a robotic arm. We show that by applying an appropriate interferometric phase correction, we can measure target displacement with the Pulson P440.

In this paper an analysis of the temporal profiles from the national ground motion map of Romania is presented. The map was built based on Sentinel-1 A/B synthetic aperture radar images acquired between 2015 – 2019, using the persistent scatterer technique, and to date, it is the first nationwide deformation map for Romania. Two types of common deformation patterns were analyzed: periodic deformation, associated with seasonal thermal dilation, and deformation with changes in the linear trend, i.e., steps and piecewise linear deformation profiles with changes of the linear trend above a threshold. The patterns were detected using methods based on statistics and signal processing.

In this paper is presented a new methodology for synthetic aperture radar images focusing called bidimensional mixed compressive sensing back-projection (CS-BP-2D). Spatial compressibility of the radar images is exploited by constructing the sparsity basis using the backprojection focusing framework and solving the reconstruction problem by means of the orthogonal matching pursuit algorithm (OMP).

The short revisit time and large coverage of the Sentinel-1 sensor have opened the way for countrywide deformation maps through persistent scatterer interferometry techniques. It is impossible for a human operator to manually verify millions of data points, hence the need for automatic analysis methods. In this paper, we expand on previous work by employing the newly developed uniform manifold approximation and projection dimensionality reduction methodology to deformation profiles, in a supervised manner, and we show that it is possible to identify certain types of profiles.

In this paper we analyze the possibility of detecting anomalous deformation profiles using supervised and unsupervised machine learning. For this we construct a simulated data set with deformation profiles, using canonical functions that model different deformation scenarios specific to possible hazardous phenomena in an urban environment. We present results for detecting anomalous profiles in the simulated data set and in real data using a clustering approach and a classifier trained on simulated deformation profiles.

Although persistent scatterer interferometry can be used for infrastructure monitoring, interpretation of the results can be difficult. We propose to use the applied element method for modelling building deformation and multiple hypothesis testing for finding deformation model in data obtained from persistent scatterer interferometry. Simulation results are presented.

In this paper, the ground stability of the city of Constanta and surrounding areas (in Romania) is analyzed using the persistent scatterer interferometry technique, applied to Sentinel-l SAR data spanning an interval of two years. Two deformation maps are computed: one where the atmospheric phase screen is estimated and removed using ERA-Interim data and spatial filtering, and another using only spatial filtering.

This paper presents results obtained by applying the maximum entropy method to image reconstruction of C-band ground-based synthetic aperture radar images. In GB-SARs, azimuth resolution is dependent on the range to target. Hence, a range dependent point spread function is synthesized. Experimental results show that through the maximum entropy method target detection is enhanced resulting in both side lobes reduction and range resolution improvement.

This paper presents the first outdoor measurements with the experimental ground based C-band synthetic aperture radar developed by the authors. A 3 meters long rail was used to generate the synthetic aperture. The results depict the first outdoor measurements, i.e. synthetic aperture radar imaging and interferometry.

This work focuses on monitoring the ground motion and infrastructure stability in an urban environment, namely in the city of Bucharest. Bucharest is a fast developing city with the average construction rate of 8-20% new buildings with respect to the existing ones. Consequently, the civil engineering industry faced new challenges related to the need of having taller buildings with deeper underground levels, a developing network of subway lines and more bridges with large diameter piles' foundations. All these new works have an important impact upon the upper ground stability. The PSI (Persistent Scatterer Interferometry) technique is used to extract the ground deformation in Bucharest from archived C-band ESA data and from more recent TerraSAR-X data. The conducted analysis constitutes historical case studies for Bucharest City covering the intervals 1992-1999, 2003-2009 and 2011-2012.