With the increase of high spatial and temporal resolution SAR data availability, novel applications and information extraction techniques become possible. Among these, the extraction of micro-motion information has the potential to unlock a range of applications, such as infrastructure monitoring, maritime surveillance and natural disaster damage assessment. However, sensors, acquisition modes and products have not been designed with in mind the optimization of micro-motion extraction and its applications, therefore, careful considerations need to take place when selecting the most suitable data and designing processing algorithms. In this paper practical and processing considerations when dealing micro-motion extraction from high-resolution SAR sensors are discussed and supported with experimental results obtained from Capella, Umbra and TerraSAR-X data.

The main problem related to Persistent Scatterer (PS) interferometry is the lack of large point clouds in rural areas, although it has proven to be a powerful tool in urban scenarios, especially in monitoring buildings with possible slow temporal deformations. The identification of PSs in low Signal to Noise Ratio (SNR) areas is crucial and can be done using a multiple hypothesis test to detect the possible presence of multiple scatterers [1]. In this paper, we frame this problem by exploiting the Kullback-Leibler Information Criterion (KLIC), developed in [2], to address the design of one-stage adaptive sensing architectures for multiple hypothesis testing problems in PS interferometry. Theoretical analysis shows the equivalence between the algorithm developed in [1] and [2] for a single scatterer. In this context we use the scheme of multiple hypothesis, provided in [2], for both the formalization of rural PS detection problem and its solution.

One of the problems associated with electromagnetic imaging is that the interaction of photons with targets occurs only on part of their surface, namely those exposed to the transmitted energy rays. Imaging of deep localized objects is very hard especially in the presence of short electromgnetic wavelengths. In this paper we propose a new method for through wall imaging, based on photons and sound waves analysis. The technique investigates Doppler analysis in terms of estimating vibrations generated on infrastructures. The proposed method estimates target's vibration energy in order to perform tomographic imaging of man-made objects, such as buildings. Unlike traditional imaging, this technique allows for through wall imaging. The experimental results are distributed over one case study, where we show the to-mographic imaging of a reinforced concrete infrastructure. We consider this preliminary work very promising for future applications performed from the processing of satellite synthetic aperture radar images.

In this letter, a new approach for the retrieval of the vertical column concentrations of trace gases from hyperspectral satellite observations is proposed. The main idea is to perform a linear spectral unmixing by estimating the abundances of trace gases’ spectral signatures in each mixed pixel collected by an imaging spectrometer in the ultraviolet region. To this aim, the sparse nature of the measurements is brought to light and the compressive sensing paradigm is applied to estimate the concentrations of the gases’ endmembers given by ana prioriwide spectral library, including reference cross sections measured at different temperatures and pressures at the same time. The proposed approach has been experimentally assessed using both simulated and real hyperspectral datasets. Specifically, the experimental analysis relies on the retrieval of sulfur dioxide during volcanic emissions using data collected by the TROPOspheric Monitoring Instrument. To validate the procedure, we also compare the obtained results with the sulfur dioxide total column product based on the differential optical absorption spectroscopy technique and the retrieved concentrations estimated using the blind source separation.

This study aims at estimating the Earth deformations due to the nuclear test carried out by North Korea on the 3rdof September 2017 by processing a time series of synthetic aperture radar images acquired by the COSMO-SkyMed satellite constellation. For active satellite sensors working in the X-band, phase information can be unreliable if scenarios with dense vegetation are observed. This uncertainty makes difficult to correctly estimate both the interferometric fringes and the information phase delay generated by the variation in the space-time domain of the atmospheric parameters. To this end, in our research we apply the Sub-Pixel Offset Tracking technique, so that the displacement information is extrapolated during the coregistration process. The results reveal an accurate estimate of the spatial displacement of similar pixels due to the nuclear explosion. The work also reveals a hypothetical underground tunnel network.

In this letter, we design a novel unsupervised architecture for automatic classification of the dominant polarization in polarimetric SAR images. To this end, we leverage the ideas developed in [1] and suitably exploit them to build a decision logic capable of recognizing the dominant scattering mechanism which characterizes the pixel under test. Specifically, we combine the original data to generate three different sets of reduced-size vectors, which feed dominant eigenvalues classifier based upon the model order selection rules. Then, the outputs of the latter classification schemes are exploited to infer, according to a specific criterion, the dominant polarization. The performance analysis is conducted on the measured data and points out the effectiveness of the newly proposed classification architecture also showing that information about the dominant polarization can be representative of the type of structure which gives raise to the dominant backscattering mechanism.

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 letter, the problem of environment classification in the radar context is addressed. Specifically, adaptive architectures are conceived to classify training data, used for covariance estimation, as either homogeneous or heterogeneous. Such architectures are based upon the generalized likelihood ratio test criterion and exploit three covariance matrix structures (i.e., Hermitian, persymmetric, and symmetric structures). Numerical examples based on both synthetic and real data confirm the effectiveness of the proposed algorithms. It is important to highlight that the proposed architectures might represent a preliminary stage whose decisions can be used to select a suitable covariance estimate for target detection purposes.

In synthetic aperture radar (SAR), the separation of the height between the ground subsidence phase components and the atmospheric phase delay mixed in the global SAR interferometry (InSAR) phase information is an issue of primary concern in the remote sensing community. This paper describes a complete procedure to address the challenge to estimate the atmospheric phase screen and to separate the three-phase components by exploiting only one InSAR image couple. This solution has the capability to process persistent scatterers subsidence maps potentially using only two multitemporal InSAR couples observed in any atmospheric condition. The solution is obtained by emulating the atmosphere compensation technique that is largely used by the global positioning system where two frequencies are used in order to estimate and compensate the positioning errors due to atmosphere parameters' variations. A sub-chirping and sub-Doppler algorithm for atmospheric compensation is proposed, which allows the successful separation of the height from the subsidence and the atmosphere parameters from the interferometric phase observed on one InSAR couple. The results are given processing images of two InSAR couples observed by the COSMO-SkyMed satellite system.

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.

In past research, the problem of maritime targets detection and motion parameter estimation has been tackled. This letter aims to contribute to preventing illegal immigration, solving the problem of obtaining reliable paths detection of targets in terms of temporal decorrelation observed on coherent change detection (CCD) maps occurring between two or more complex-valued synthetic aperture radar (SAR) images. Most detection problems are related to terrain clutter and platform motion instabilities which make the paths structure detection and reconnaissance difficult. This letter presents a complete procedure called low-rank plus sparse decomposition radon transform (RT) CCD for automatic estimation and tracking of target paths by evaluating the generated temporal decorrelation CCD-SAR images, observed at the desert environments. The algorithm consists of evaluating a dual-stage low-rank plus sparse decomposition (LRSD) assisted by RT for clutter reduction, sparse object detection, and precise path inclination estimation. 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 incoherent patterns generated by targets from the unchanging low-rank and more coherent background. This dual-stage RPCA and RT applied to SAR-CCD surveillance permits fast detection and enhanced parameter estimation of terrain target paths.

We present a detailed survey on 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 unstable due to the underlying geology that is formed by alternate and variable strata of highly soluble materials such as gypsum, anhydrite, marl and limestone. Here we present the first comprehensive multi-sensor cumulative deformation map for the dam generated from space-based synthetic aperture radar (SAR) measurements from the Italian constellation COSMO-SkyMed and the European Sentinel-1a satellite. We compared 2014-2016 data to an historic dataset spanning 2004-2010 acquired with the Envisat ASAR sensor. We found that deformation was rapid during 2004-2010, slowed down in 2012-2014, and restarted in August 2014 when grouting operations stopped due to the temporary capture of the dam by the self proclaimed Islamic State in Iraq and Syria (ISIS). We took advantage of the availability of data from multiple SAR satellites to infer the deformation at the dam in great spatial and temporal detail and shed new light on the processes of the ongoing destabilization. This study highlights how new constellations of SAR sensors together with the availability of historical datasets are leading to important advances in deformation monitoring of small scale geologic and manmade features.

The problem of chirped synthetic aperture radar (SAR) systems is the high vulnerability of the received information to electromagnetic (EM) attacks. This letter proposes a valid recovery solution for SAR single-look complex images that are corrupted by noncoherent EM noise covering only the higher frequency spectrum. The solution consists of, first, exporting the spectrum damages that occur in the native data and, second, focusing only the survived spectrum information at lower resolution. The recovery of the original image is done by super-resolution signal processing based on spectrum extrapolation and implemented by convex programming.

In the Multi-Baseline SAR tomography remote sensing technique, the tomographic resolution is proportional to the vertical aperture component of the synthetic antenna. In order to avoid the problem of obtaining aliased tomographic results when designing multi-baseline SAR acquisition geometries using the fewest number of repeated radar tracks, it is necessary to process the data-set by advanced signal processing techniques that can properly process coherent and distributed composed environments SAR data. In this paper the Digital Gabor Transform (DGT) decomposition for sparsity seeking and the Compressed Sensing (CS) for signal recovery techniques performance will be analyzed. Recovery in highly over-complete dictionaries leads to large-scale optimization problems that can be successfully reached specially because of recent advances in linear and quadratic programming by Interior Point Methods (IPM). This paper considers the Convex Optimization (CVX) tomographic solution in order to process multi-baseline datasets over forested environments, in a Fourier under-sampled configuration. In this situation, the vertical reflectivity function is in a smooth domain. The DGT is a suitable method in order to generate an over-complete dictionary for sparsity seeking. The CVX Second Order Cone Programming Solution (SOCPs) by IPM using a generic log-barrier algorithm has been tested in order to optimize the dictionary atoms. In particular the following recovery technique has been implemented: l1 norm minimization with quadratic constraints (L1QC). This technique has been validated over real forested areas pointing out the better performance of the proposed solution in such a particular environment.