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Multi-temporal SAR interferometry (MTInSAR), allows analysing wide areas, identifying critical ground instabilities, and studying the phenomenon evolution in a long time-scale. The identification of MTInSAR displacements trends showing non-linear kinematics is of particular interest since they include warning signals related to pre-failure of natural and artificial structures. Recently, the authors have introduced two innovative indexes for characterising MTInSAR time series: one relies on the fuzzy entropy and measures the disorder in a time series [1], the other performs a statistical test based on the Fisher distribution for selecting the polynomial model that more reliably approximate the displacement trend [2].This work reviews the theoretical formulation of these indexes and evaluate their performances by simulating time series with different characteristics in terms of kinematic (stepwise linear with different breakpoints and velocities), level of noise, signal length and temporal sampling. Finally, the proposed procedures are used for analysing displacement time series derived by processing Sentinel-1 and COSMO-SkyMed datasets acquired over Southern Italian Apennine (Basilicata region), in an area where several landslides occurred in the recent past. The MTInSAR displacement time series have been analysed by using the proposed methods, searching for nonlinear trends that are possibly related to relevant ground instabilities and, in particular, to potential early warning signals for the landslide events. Specifically, the work presents an example of slope pre-failure monitoring on Pomarico landslide, an example of slope post-failure monitoring on Montescaglioso landslide, and few examples of structures (such as buildings and roads) affected by instability related to different causes.References[1] Refice, A.; Pasquariello, G.; Bovenga, F. Model-Free Characterization of SAR MTI Time Series. IEEE Geosci. Remote Sens. Lett. 2020, doi:10.1109/lgrs.2020.3031655.[2] Bovenga, F.; Pasquariello, G.; Refice, A. Statistically‐based trend analysis of mtinsar displacement time series. Remote Sens. 2021, doi:10.3390/rs13122302.Acknowledgments This work was supported in part by the Italian Ministry of Education, University and Research, D.D. 2261 del 6.9.2018, Programma Operativo Nazionale Ricerca e Innovazione (PON R&I) 2014–2020 under Project OT4CLIMA; and in part by Regione Puglia, POR Puglia FESR-FSE 204-2020 - Asse I - Azione 1.6 under Project DECiSION (p.n. BQS5153).

Images acquired by RGB cameras on Unmanned Aerial Vehicles (UAVs) can be particularly useful to detect crowd in urban areas when restrictive conditions are imposed for the sake of public safety or health , such as during the Covid 19 pandemic. Together with acquired images, opportune pattern recognition techniques have to be considered to extract useful information. In this framework, features capturing the semantic rich information encompassed in VHR images have to be computed. In particular, Deep Neural Networks (DNN) have been recently proved to be able to extract useful features from data [1]. Moreover, in a transfer learning approach, a DNN pre-trained on a data set can be used to extract opportune features, named deep-features, from another data set, belonging to a different applicative domain [1], [2]. Here, a transfer learning technique is presented to produce change maps, detecting how people gathering increases, from VHR images. It is based on deep-features computed by using some pre-trained convolutional layers of AlexNet. The proposed methodology has been tested on a data set composed of several synthetic VHR images, that simulate crowd collecting in a park, as they can be acquired by RGB camera on a UAV flying at 10 meters height from the ground. The experimental results show that the proposed technique is able to efficiently detect change due to new people incoming in the scene or people that get away, with a low computational cost and in a near-real time operative mode.

Multi-temporal SAR interferometry (MTInSAR) allows analysing wide areas, identifying critical ground instabilities, and studying the phenomenon evolution in a long time-scale. Nowadays satellite SAR constellations provide datasets covering time periods of several years with short revisit times, which allow investigating ground displacements showing non-linear kinematics. These are particularly interesting since they include warning signals related to pre-failure of natural and artificial structures. Recently, approaches have been proposed for recognising and analysing nonlinear displacements, which use different strategies. The authors have introduced two innovative indexes for characterising MTInSAR time series: one relies on the fuzzy entropy and measures the disorder in a time series, the other performs a statistical test based on the Fisher distribution for selecting the polynomial model that more reliably approximate the displacement trend. This work reviews the theoretical formulation of these indexes and evaluate their performances by simulating time series with different characteristics in terms of kinematic, level of noise, signal length and temporal sampling. Finally, the proposed procedures are used for analysing displacement time series derived by processing real datasets acquired by both Sentinel-1 and COSMO-SkyMed constellations. In particular the hilly villages of Pomarico and Montescaglioso have been investigated, which are located in Southern Italian Apennine (Basilicata region), in an area where several landslides occurred in the recent past, causing damages to houses, commercial buildings, and infrastructures. The MTInSAR displacement time series have been analysed by using the proposed methods, searching for nonlinear trends that are possibly related to relevant ground instabilities and, in particular, to potential early warning signals for the landslide events affecting Mtescaglioso in 2013 and Pomarico in 2019. Acknowledgments - This work was supported in part by the Italian Ministry of Education, University and Research, D.D. 2261 del 6.9.2018, Programma Operativo Nazionale Ricerca e Innovazione (PON R&I) 2014–2020 under Project OT4CLIMA.

Current multi-temporal interferometric Synthetic Aperture Radar (MTInSAR) datasets cover long time periods with regular temporal sampling. This allows high-rate and non-linear trends to be observed, which typically characterize pre-failure warning signals. In order to fully exploit the content of MTInSAR products, methods are needed for the automatic identification of relevant changes along displacement time series and the classification of the targets on the ground according to their kinematic regime. This work reviews some of the classical procedures for model ranking, based on statistical indices, which are applied to the characterization of MTInSAR displacement time series, and introduces a new quality index based on the Fisher distribution. Then, we propose a procedure to recognize automatically the minimum number of parameters needed to model a given time series reliably within a predefined confidence level. The method, though general, is explored here for polynomial models, which can be used in particular to approximate satisfactorily and with computational efficiency the piecewise linear trends that are generally used to model warning signals preceding the failure of natural and artificial structures. The algorithm performance is evaluated under simulated scenarios. Finally, the proposed procedure is also demonstrated on displacement time series derived by the processing of Sentinel-1 data.

The work proposes a new set of rules based on statistical characterization of displacement time series, which allows, under certain constraints, recognising automatically different kinematic classes, and estimating the relevant parameters useful for target characterization in time. We introduce a new statistical test based on the Fisher distribution aimed at evaluating the reliability of a displacement model with a certain statistical confidence. We also studied the reliability of other tests already introduced in literature and used for comparing two different models, namely the Akaike Information Criterion, the Bayesian Information Criterion, and the Fisher test. A performance analysis has been carried out by simulating piecewise linear displacement time series with different characteristics in terms of kinematic, level of noise, and signal length. The displacement estimations performed by using the different tests have been compared. Finally, a procedure for selecting the optimum displacement model has been defined according to the output of the performance analysis. The proposed procedure has been also tested by using displacement time series derived from Sentinel-1 datasets.

DAFNE(Data Fusion by Bayesian Network) is a Matlab-based open source toolbox, conceived to produce flood maps from remotely sensed and other ancillary information, through a data fusion approach [1]. It is based on Bayesian Networks and it is composed of five modules, which can be easily modified or upgraded to meet different user needs. DAFNE provides, as output products, probabilistic flood maps, i.e., for each pixel in a given output map, the probability value that the corresponding area has been reached from the inundation is reported. Moreover, if remote sensed images have been acquired in different days during a flood event, DAFNE allows to follow the inundation temporal evolution.It is well known that flood scenarios are typical examples of complex situations in which different factors have to be considered to provide accurate and robust interpretation of the situation on the ground [2]. In particular, the combined analysis of multi-temporal and multi-frequency SAR intensity and coherence trends, together with optical data and other ancillary information, can be particularly useful to map flooded area, characterized by different land cover and land use [3]. Here a recent upgrade is presented that allows to consider as input data multi-frequency SAR intensity images, such as X-band, C-band and L-band images.Three different inundation events have been considered as applicative examples: for each one, multi-temporal probabilistic flood maps have been produced by combining multi-temporal and multi-frequency SAR intensity images images (such as COSMO-SkyMed , Sentinel-1 images and ALOS 2 images), InSAR coherence and optical data (such as Landsat 5 images or High Resolution images), together with geomorphic and other ground information. Experimental results show good capabilities of producing accurate flood maps with computational times compatible with a near real time application. [1] A. D’Addabbo, A. Refice, F. Lovergine, G. Pasquariello, DAFNE: A Matlab toolbox for Bayesian multi-source remote sensing and ancillary data fusion, with application to flood mapping. Computer and Geoscience 112 (2018), 64-75.[2] A. Refice et al, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 7, no. 7, pp. 2711–2722, 2014.[3] A. D’Addabbo et al., “A Bayesian Network for Flood Detection combining SAR Imagery and Ancillary Data,” IEEE Transactions on Geoscience and Remote Sensing, vol.54, n.6, pp.3612-3625, 2016.

Multi-temporal SAR interferometry (MTInSAR) provides mean displacement maps and displacement time series over coherent objects on the Earth surface, allowing analysis of wide areas to identify ground deformations, and studying evolution of displacement phenomena over long time scales. MTInSAR techniques have proven very useful for detecting and monitoring also slope instabilities.Nowadays, several satellite missions are available providing InSAR data at different wavelengths, spatial resolutions, and revisit times. The Italian X-Band COSMO-SkyMed constellation acquires data with spatial resolution reaching metric values, and provides revisit times of up to a few days, leading to an increase in the density of the measurable targets, thus improving the monitoring of local scale events as well as the detection of non-linear displacements. The recent Sentinel-1 C-band mission from the European Space Agency (ESA) provides a spatial resolution comparable to previous ESA SAR missions, but a nominal revisit time reduced to 6 days. By offering regular global-scale coverage, better temporal resolution and freely available imagery, Sentinel-1 improves the performance of MTInSAR for ground displacement investigations. In particular, the short revisit time allows a better time series analysis by improving the temporal sampling and thus the chances to catch pre-failure signals characterised by high rate and non-linear behaviour. Moreover, it allows collecting large data stacks in a short time periods, thus improving MTInSAR performance in emergency (post-event) scenarios. These characteristics are very promising for early warning of slope failure events and monitoring subsequent displacements trends. In this work, we present the results obtained by using both COSMO-SkyMed and Sentinel-1 data for investigating the ground stability of hilly villages located in Southern Italian Apennine (Basilicata region). In the area of interest, several landslides occurred in the recent past (e.g. Montescaglioso in 2013) and more recently (e.g. Pomarico in 2019), causing extensive damage to houses, commercial buildings, and infrastructures.SAR datasets acquired by COSMO-SkyMed and Sentinel-1 from both ascending and descending orbits have been processed by using the SPINUA MTInSAR algorithm, in order to exploit the potentials of these two satellite missions to investigate ground displacements related to slope instabilities. Mean velocity maps and displacement time series have been analysed looking, in particular, for non-linear trends that are possibly related to relevant ground instability episodes and, thanks to the high spatial resolution, useful in terms of early warning, in the case of rigid soil masses. Results are presented and discussed in relation to known events occurred in the area of interest.

Oggetto del documento è l'individuazione delle applicazioni di osservazione della Terra che meglio si prestano all'utilizzo di dati ottici acquisiti da una costellazione di satelliti in orbite molto basse con le caratteristiche della missione in oggetto. In particolare saranno esplorate le possibilità di avere: i) bassi tempi di rivisitazione grazie all'uso di più satelliti, ii) alta risoluzione geometrica e/o radiometrica; e di definire dei requisiti necessari all'implementazione delle applicazioni individuate in termini di: i) specifiche radiometriche dei sensori, ii) geometrie di acquisizione, iii) caratteristiche orbitali dei satelliti della costellazione.

Oggetto del documento è la descrizione dell'applicazione degli algoritmi sviluppati nella prima parte del progetto, volti alla caratterizzazione statistica di serie temporali da dati MTI, ad un dataset di esempio.

High-resolution, remotely sensed images of the Earth surface have been proven to be of help in producing detailed flood maps, thanks to their synoptic overview of the flooded area and frequent revisits. However, flood scenarios can be complex situations, requiring the integration of different data in order to provide accurate and robust flood information. Several processing approaches have been recently proposed to efficiently combine and integrate heterogeneous information sources. In this paper, we introduce DAFNE, a Matlab®-based, open source toolbox, conceived to produce flood maps from remotely sensed and other ancillary information, through a data fusion approach. DAFNE is based on Bayesian Networks, and is composed of several independent modules, each one performing a different task. Multi-temporal and multi-sensor data can be easily handled, with the possibility of following the evolution of an event through multi-temporal output flood maps. Each DAFNE module can be easily modified or upgraded to meet different user needs. The DAFNE suite is presented together with an example of its application.

In multi-temporal applications of synthetic aperture radar SAR interferometry, differential phase contributions due to atmospheric inhomogeneities, estimated over sparse points, have to be interpolated and removed from the regular-grid interferograms in order to highlight the phase stability of more image pixels, which then add to the available data to infer useful information about terrain displacements or other phenomena of interest. Interpolation is usually done on the phase data after a phase unwrapping PU operation. In a previous work, we considered the alternative interpolation step applied directly to the complex phasor derived from the wrapped phase, thus bypassing the error-prone sparse PU operation. In this article, the performances of the proposed methodology are evaluated over atmospheric phase screen APS data estimated from a previous processing through persistent scatterers interferometry PSI methods. The original persistent scatterer PS population is reduced by thresholding their inter-image coherence values, and then further subsampled randomly in a rectangle inside a detected subsidence bowl. Both the classical and the proposed interpolation procedures are applied to the subsampled APS phase values. The interpolated fields are then removed from the rest of the PS, and the residual phase values are compared in terms of inter-image coherence. Results confirm that interpolating complex phasors, thus avoiding PU, gives results equivalent to the standard procedure in good sampling conditions. Moreover, when point sparsity induces phase aliasing, thus hindering the PU operation, the proposed method allows to better recover phase information over unsampled pixels, improving the final results of the PSI processing.

Multi-temporal SAR interferometry (MTInSAR) techniques are able to derive displacement maps and displacement time series over coherent objects on the Earth, used for monitoring either geophysical ground deformation or structural instabilities. Nowadays, several datasets are available at different wavelengths, spatial resolutions, and revisit time, collectively covering long time periods (even more than 20 years). In particular, short revisit times, by improving the temporal sampling, make theoretically possible to catch high-rate and non-linear kinematics, which typically characterize warning signals like, for instance, those related to pre-failure of artificial infrastructures. However, MTInSAR algorithms generally fit the displacement signal by using a linear model, which is computationally convenient, and also, more robust than higher-order models for what concerns the errors affecting the InSAR phase. Moreover, the analysis of the MTInSAR products is often performed by only considering the mean displacement rate, computed over the monitoring period, which is the information typically displayed on the displacement maps. However, this approach suffer for two main limitations: 1) by analysing time series covering long time periods (as more and more common with the datasets available nowadays), it is more probable that nonlinear displacements occur; 2) the mean displacement rate, though useful for distinguishing between stable and unstable areas on the ground, often mask interesting nonlinear signals, useful for early warning. Therefore, in order to fully exploit the content of MTInSAR products, methods are needed for automatically identifying relevant changes along displacement time series, and, consequently, classifying the targets on the ground according to their kinematic regime. This also allows performing a more reliable ground deformation spatial analysis, by distinguishing among spatial patterns of different kinematics. Recently, approaches have been proposed to tackle this problem, which use different strategies. This work presents some general considerations and a possible new procedure based on statistical characterization of displacement time series, which allows, under certain constraints, recognising automatically different kinematic classes, and estimating the relevant parameters useful for target characterization in time. The theoretical formulation of the proposed method is introduced, then the algorithm performance is evaluated under simulated scenarios, by varying simulation parameters such as type of kinematic model, temporal sampling rate, number of observations, and signal to noise ratio. Finally, some results of the analysis of displacement time series derived by MTInSAR processing of real datasets are presented.

Multi-temporal SAR interferometry (MTInSAR), by providing both mean displacement maps and displacement time series over coherent objects on the Earth, allows analysing wide areas, identifying critical ground instabilities, and studying the phenomenon evolution in a long time-scale. This technique has been proved to be very useful for detecting and monitoring also slope instabilities. Nowadays, several satellite missions are available providing InSAR data at different wavelengths, spatial resolutions, and revisit time. The high-resolution X-Band COSMO-SkyMed constellation, acquires data with spatial resolution reaching metric values, and provides revisit times of up to a few days, leading to an increase in the density of the measurable targets, thus improving the monitoring of local scale events as well as the detection of non-linear displacements. The recent Sentinel-1 C-band mission from the European Space Agency (ESA), provides a spatial resolution comparable to the previous ESA missions, but a nominal revisit time reduced to 6 days. By offering regular global-scale coverage, better temporal resolution and freely available imagery, Sentinel-1 improves the performance of MTInSAR for ground displacement investigations. In particular, the short revisit time allows a better time series analysis by improving the temporal sampling and the chances to catch pre-failure signals characterised by high rate and non-linear behaviour signals. Moreover, it allows collecting large data stacks in a short time period, thus improving the MTInSAR performance in emergency (post-event) scenarios. These characteristics are very promising for early warning of slope failure events and monitoring subsequent displacements trends. In this work, we present the results obtained by using both COSMO-SkyMed and Sentinel-1 data for investigating the ground stability of hilly villages located in Southern Italian Apennine (Basilicata region). In the area of interest, several landslides occurred in the recent past (e.g. on Montescaglioso in 2013) and also very recently (e.g. on Pomarico in 2019), causing damages to houses, commercial buildings, and infrastructures. SAR datasets acquired by COSMO-SkyMed and Sentinel-1 from both ascending and descending orbits have been processed by using SPINUA MTInSAR algorithm, in order to exploit the potentials of these two satellite missions to investigate ground displacements related to the slope instabilities. Mean velocity maps and displacement time series have been analysed looking, in particular, to non-linear trends that are possibly related to relevant ground instabilities and, thanks to the high spatial resolution, useful in terms of early warning, in the case of rigid soil masses. Results are presented and discussed in relation to the well know events occurred in the area of interest.

Multi-temporal SAR interferometry (MTInSAR), by providing both mean displacement maps and displacement time series over coherent objects on the Earth, allows analysing wide areas, identifying critical ground instabilities, and studying the phenomenon evolution in a long time-scale. This technique has been proved to be very useful for detecting and monitoring also slope instabilities. Nowadays, several satellite missions are available providing InSAR data at different wavelengths, spatial resolutions, and revisit time. The high-resolution X-Band COSMO-SkyMed constellation, acquires data with spatial resolution reaching metric values, and provides revisit times of up to a few days, leading to an increase in the density of the measurable targets, thus improving the monitoring of local scale events as well as the detection of non-linear displacements. The recent Sentinel-1 C-band mission from the European Space Agency (ESA), provides a spatial resolution comparable to the previous ESA missions, but a nominal revisit time reduced to 6 days. By offering regular global-scale coverage, better temporal resolution and freely available imagery, Sentinel-1 improves the performance of MTInSAR for ground displacement investigations. In particular, the short revisit time allows a better time series analysis by improving the temporal sampling and the chances to catch pre-failure signals characterised by high rate and non-linear behaviour signals. Moreover, it allows collecting large data stacks in a short time period, thus improving the MTInSAR performance in emergency (post-event) scenarios. These characteristics are very promising for early warning of slope failure events and monitoring subsequent displacements trends. In this work, we present the results obtained by using both COSMO-SkyMed and Sentinel-1 data for investigating the ground stability of hilly villages located in Southern Italian Apennine (Basilicata region). In the area of interest, several landslides occurred in the recent past (e.g. on Montescaglioso in 2013) and also very recently (e.g. on Pomarico in 2019), causing damages to houses, commercial buildings, and infrastructures. SAR datasets acquired by COSMO-SkyMed and Sentinel-1 from both ascending and descending orbits have been processed by using SPINUA MTInSAR algorithm, in order to exploit the potentials of these two satellite missions to investigate ground displacements related to the slope instabilities. Mean velocity maps and displacement time series have been analysed looking, in particular, to non-linear trends that are possibly related to relevant ground instabilities and, thanks to the high spatial resolution, useful in terms of early warning, in the case of rigid soil masses. Results are presented and discussed in relation to the well know events occurred in the area of interest.

La rilevazione dei cambiamenti ambientali da dati telerilevati è ampiamente usata in molti contesti applicativi, sia per effettuare un controllo periodico e sistematico del territorio (variazione dell'estensione abitativa, monitoraggio delle zone vegetate, etc.) sia per la gestione ed il controllo di situazioni di emergenza dovute al verificarsi di disastri naturali, quali frane, alluvioni, incendi [1]. Per catturare qualsiasi cambiamento ambientale, è indispensabile considerare immagini multi-temporali della stessa area geografica [2]. L'obiettivo dell'attività 3.2 è stato quello di progettare e realizzare una metodologia, matematicamente e statisticamente ben fondata, in grado di fondere immagini multi-temporali relative alla stessa area geografica con l'obiettivo di rilevare in modo automatico i cambiamenti a terra.

Oggetto del documento è di descrivere lo sviluppo e l'implementazione di algoritmi per la caratterizzazione di serie temporali di dati DInSAR. L'obbiettivo specifico dell'attività progettuale è quella di sviluppare una metodologia in grado di identificare le caratteristiche spazio-temporali delle serie di deformazione rilevanti per stabilire l'integrità di una rete di tubazioni sotterranee. Lo stato dell'arte relativo ai metodi di caratterizzazione delle serie temporali [1] ha evidenziato una serie di knowledge gap nella caratterizzazione di serie di dati SAR da tecniche MTI. Sulla base della rassegna effettuata nel pacco di lavoro precedente e descritta in [1], si è proceduto a ideare, testare e realizzare un prototipo di una catena di processing per la caratterizzazione si serie temporali MTI. Il presente documento descrive l'implementazione algoritmica della suddetta catena di processing.