We present a detailed survey of the ongoing destabilization process of the Mosul dam. The dam is located on the Tigris river and is the biggest hydraulic structure in Iraq. From a geological point of view the dam foundation is poor due to a site geology formed by alternating strata of highly soluble materials including gypsum, anhydrite, marl and limestone. Here we present the first multi-sensor cumulative deformation map for the dam generated from space-based interferometric synthetic aperture radar measurements from the Italian constellation COSMO-SkyMed and the European sensor Sentinel-1a over the period 2014–2016 that we compare to an older dataset spanning 2004–2010 acquired with the European Envisat satellite. We found that deformation was rapid during 2004–2010, slowed in 2012–2014 and increased since August 2014 when grouting operations stopped due to the temporary capture of the dam by the self proclaimed Islamic State. We model the inferred deformation using a Markov chain Monte Carlo approach to solve for change in volume for simple tensile dislocations. Results from recent and historical geodetic datasets suggests that the volume dissolution rate remains constant when the equivalent volume of total concrete injected during re-grouting operations is included in the calculations.

The problem in obtaining stable motion estimation of maritime targets is that sea clutter makes wake structure detection and reconnaissance difficult. This letter presents a complete procedure for the automatic estimation of maritime target motion parameters by evaluating the generated Kelvin waves detected in synthetic aperture radar (SAR) images. The algorithm consists in evaluating a dual-stage low-rank plus sparse decomposition (LRSD) assisted by Radon transform (RT) for clutter reduction, sparse object detection, precise wake inclination estimation, and Kelvin wave spectral analysis. The algorithm is based on the robust principal component analysis (RPCA) implemented by convex programming. The LRSD algorithm permits the extrapolation of sparse objects of interest consisting of the maritime targets and the Kelvin pattern from the unchanging low-rank background. This dual-stage RPCA and RT applied to SAR surveillance permits fast detection and enhanced motion parameter estimation of maritime targets.

Large infrastructures need continuous maintenance because of materials degradation due to atmospheric agents and their persistent use. This problem makes it imperative to carry out persistent monitoring of infrastructure health conditions in order to guarantee maximum safety at all times. The main issue of early warning infrastructure fault detection is that expensive in-situ distributed monitoring sensor networks have to be installed. On the contrary, the use of satellite data has made it possible to use immediate and low-cost techniques in recent years. In this regard, the potential of spaceborne Synthetic Aperture Radar for the monitoring of critical infrastructures is demonstrated in geographically extended areas, even in the presence of clouds, and in really tough weather. A complete procedure for damage early-warning detection is designed, by using micro-motion (m-m) estimation of critical sites, based on modal proprieties analysis. Particularly, m-m is processed to extract modal features such as natural frequencies and mode shapes generated by vibrations of large infrastructures. Several study cases are here considered and the “Morandi” Bridge (Polcevera Viaduct) in Genoa (Italy) is analyzed in depth highlighting abnormal vibration modes during the period before the bridge collapsed.

In past research, the problem of obtaining stable motion estimation of maritime targets in sea clutter making wake structure detection and reconnaissance difficult has been tackled. This new research presents an upgrade for automatic estimation of maritime target motion parameters by evaluating the generated Kelvin waves detected in synthetic aperture radar (SAR) images. The algorithm consists in considering the polarimetric (Pol) information of SAR images and evaluating a multiple-channel and dual-stage Pol low-rank plus sparse decomposition (Pol-LRSD) assisted by Radon transform (RT) for clutter reduction, sparse object detection, precise wake inclination estimation, and targets classification. This upgraded algorithm is based on Pol robust principal component analysis (Pol-RPCA) implemented by convex programming. This upgraded Pol-LRSD algorithm permits the extrapolation of the Pol signature of sparse objects of interest consisting of the maritime targets and the Kelvin pattern from the unchanging low-rank background. Pol-RPCA and RT methods applied to Pol SAR surveillance permit more precise detection and segmentation of maritime targets.

In this paper, we develop a new elegant systematic framework relying on the Kullback-Leibler Information Criterion to approach the design of one-stage adaptive detection architectures for multiple hypothesis testing problems in radar. Specifically, at the design stage, we assume that one out of several alternative hypotheses may be in force and that only one null hypothesis exists. Then, starting from the case where all the parameters are known and proceeding towards the adaptivity with respect to the entire parameter set, we come up with decision schemes for multiple alternative hypotheses consisting of the sum between the compressed log-likelihood ratio based upon the available data and a penalty term accounting for the number of unknown parameters. Such a term arises from suitable approximations of the Kullback-Leibler Divergence between the true and a candidate probability density function. Interestingly, under specific constraints, the proposed decision schemes can share the constant false alarm rate property by virtue of the Invariance Principle. Finally, we also show that the new architectures can be viewed as the result of a suitable regularization of the log-likelihood.

The capability of sensors to identify individuals in a specific scenario is a topic of high relevance for sensitive sectors such as public security. A traditional approach involves cameras; however, camera-based surveillance systems lack discretion and have high computational and storing requirements in order to perform human identification. Moreover, they are strongly influenced by external factors (e.g., light and weather). This paper proposes an approach based on a temporal convolutional deep neural networks classifier applied to radar micro-Doppler signatures in order to identify individuals. Both sensor and processing requirements ensure a low size weight and power profile, enabling large scale deployment of discrete human identification systems. The proposed approach is assessed on real data concerning 106 individuals. The results show good accuracy of the classifier (the best obtained accuracy is 0.89 with an F1-score of 0.885) and improved performance when compared to other standard approaches.