Multiple-stage adaptive architectures are conceived to face with the problem of target detection buried in noise, clutter, and intentional interference. First, a scenario where the radar system is under the electronic attack of noise-like interferers is considered. In this context, two sets of training samples are jointly exploited to devise a novel two-step estimation procedure of the interference covariance matrix. Then, this estimate is plugged in the adaptive matched filter to mitigate the deleterious effects of the noise-like jammers on radar sensitivity. Besides, a second scenario, which extends the former by including the presence of coherent jammers, is addressed. Specifically, the sparse nature of data is brought to light and the compressive sensing paradigm is applied to estimate target response and coherent jammers amplitudes. The likelihood ratio test, where the unknown parameters are replaced by previous estimates, is designed and assessed. Remarkably, the sparse approach allows for echo classification and estimation of both angles of arrival and number of the interfering sources. The performance analysis, conducted resorting to simulated data, highlights the effectiveness of the newly proposed architectures also in comparison with suitable competing architectures (when they exist).

With the entry into operation of the Sentinel-2 mission in June 2015, a new land monitoring costellation of twin satellites has been added to Copernicus project from ESA and new insights have been derived through the combination of Sentinel-2 data with other optical/multispectral data, and with other data from satellites belonging to the same Copernicus project. To this end, the objective of this paper has been to present new added-value tools first through the integration of different satellite platforms: data from NASA Landsat-8 and ESA Sentinel-1 have been used and combined, and furthermore through the comparison of satellite data all from the same Copernicus project: data from Sentinel-1 and Sentinel-2 have been jointly processed and compared. Although data from optical/multispectral sensors, as those of Landsat-8 and Sentinel-2, and data from SAR on board of Sentinel-1, are very different, their combination provides useful and interesting results. The integration and combination of these data can find useful application in many fields from oceans to waterways, from land surfaces to fossil deposits, from vegetation to forest areas. In this works authors have focused their interest in green areas and vegetation monitoring applications, by choosing as case of interest the Royal Palace of Caserta and its gardens. The idea has started from the increasing interest in monitoring the cultural heritage monuments and in particular the surrounding vegetation with the green areas and the parks inside. Satellite images can put into evidence boundaries modifications, the vegetation state, their possible degradation, and other phenomena such as changes in the territories due both to natural and to anthropogenic causes. Data combination from different sources as above specified gives a good number of indexes very useful to analyze the vegetation state and its health in a very deep way. Many of these indexes have been calculated and discussed for investigation.

Dam monitoring represents a crucial issue in order to avoid catastrophic failures due to infrastructure aging or earthquake damages. Differential SAR Interferometry (DInSAR) is a technique suitable for critical infrastructure monitoring, also for the availability of free data and tools, that can be used by experts in SAR remote sensing and also by geologists and civil engineers, after having acquired the right confidence and experience in these data processing and tool use. In order to apply the DInSAR technique, in its basic and simple version, to critical infrastructure monitoring, it is very important to assess its performance. Nevertheless, validation results are not largely available in literature, because heterogeneous technical competencies are required to this aim and in situ measurements must be collected and made available. In this paper, we propose a highly reproducible DInSAR workflow that can be effectively used for dam monitoring, by validating its results with in situ measurements on some significant case studies in Italy.

In the last two decades, the increased production and installation of photovoltaic (PV) plants worldwide has asked for efficient low-cost methods for PV plant inspection to monitor their functionality and guaranteed their performance. To lower maintenance costs new systems have been thought to substitute human workers inspecting the PV plants. The employment of Unmanned Aerial Vehicles (UAVs) has allowed realizing a fast detection of defects and problems arisen in PV plants thanks to the fusion of computer vision algorithms and high accuracy Global Navigation Satellite System (GNSS) positioning techniques able to detect and tag anomalies and identify the defective panels. Authors in this paper intend to present the state-of-the-art in the Computer Vision field applied to PV plant inspection and to thermal anomalies detection over the panels. In addition, different data sets have been recorded and compared for geo-referencing the solar panels. They have been derived through the U-blox NEO-M8N installed on board of the UAV used for inspection. Although the U-blox NEO-M8N measures are less accurate than the classic RTK GNSS ones, the measurements obtained with this handset introduce a very interesting novelty since initial services of the Galileo constellation, supported by the NEO-M8N GNSS module, have become available only since last December. Future testing and validation will be performed by using geo-referenced data from the RTK GNSS receiver, that has been ordered with a specially customized antenna whose specifications have been properly designed and sent to the manufacturer for its fabrication. Next campaigns will allow to get results also from this RTK receiver and to properly validate the proposed algorithm, by comparing new results with those found through the employment of U-blox receiver.

In this paper, we address the problem of classifying clutter returns into statistically homogeneous subsets. The classification procedures are devised assuming latent variables, which represent the classes to which each range bin belongs, and three different models for the structure of the clutter covariance matrix. Then, the expectation-maximization algorithm is exploited in conjunction with cyclic estimation procedures to come up with suitable estimates of the unknown parameters. Finally, the classification is performed by maximizing the posterior probability that a range bin belongs to a specific class. The performance analysis of the proposed classifiers is conducted over synthetic data as well as real recorded data and highlights that they represent a viable means to cluster clutter returns with respect to their range.

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.