Indonesia is periodically affected by severe volcanic eruptions and earthquakes, which are geologically coupled to the
convergence of the Australian tectonic plate beneath the Sunda Plate. Multi-temporal SAR interferometry (MTI) can be
used to support studying and modelling of terrain movements. This work is aimed at performing an analysis of ground
displacements over Indonesian sites through MTI techniques. Test sites have been selected according to the availability
of archived SAR data, GNSS networks, and geological data. A stack of COSMO-SkyMed data, acquired in stripmap
mode between 2011 and 2015, has been selected over the Banda Aceh region in Sumatra island. Geological maps of the
test sites are available, and several GNSS stations from the Continuously Operating Reference Stations Indonesian
network are found in the area of interest. Both the SPINUA and the StaMPS MTI algorithms have been used for
processing the data, and deriving displacement maps. The ground deformations detected on the area are interpreted
according to the available geological and geophysical information. The MTI results seem to confirm the inactivity of the
Aceh fault segment, while the lack of coherent targets hinders reliable displacement measurements along the Seulineum
segment. MTI data additionally allowed to identify local, non-tectonic ground instabilities: several areas are affected by
subsidence due to unconsolidated coastal and alluvial sediments, deserving more investigations by local authorities.
Finally, MTI results could be useful to integrate and update data from the existing GPS network.
Multi-temporal InSAR (MTI) applications pose challenges related to the availability of coherent scattering from the ground surface, the complexity of the ground deformations, the atmospheric artifacts, the visibility problems related to the ground elevation. Nowadays, several satellite missions are available providing interferometric SAR data at different wavelengths, spatial resolutions, and revisit time. A new interesting opportunity is provided by Sentinel-1 mission, which has a spatial resolution comparable to previous ESA C-band missions, and revisit times reduced to up to 6 days. It is envisioned that, by offering regular, global-scale coverage, improved temporal resolution and freely available imagery, Sentinel-1 will guarantee an increasing use of MTI for ground displacement investigations. According to these different SAR space-borne missions, the present work discusses current and future opportunities of MTI applications to ground instability monitoring. Issues related to coherent target detection and mean velocity precision will be addressed through a simple theoretical model assuming backscattering mechanisms related to point scatterers. The paper also presents an example of multi-sensor ground instability investigation over the site of Marina di Lesina, Southern Italy, a village lying over a gypsum diapir, where a hydration process, involving the underlying anhydride, causes a smooth uplift pattern affecting the entire village area, and the formation of scattered sinkholes. More than 20 years of MTI SAR data have been used, coming from both legacy ERS and ENVISAT missions, and last-generation Radarsat-2, COSMO-SkyMed, and Sentinel-1A sensors.
The COSMO-SkyMed (CSK) constellation acquires data from its four SAR X-band satellites in several imaging modes,
providing in particular different view angles. The present work investigates the potential of CSK constellation for ground
elevation measurement through SAR radargrammetry. We selected an area around Parkfield (California), where several
CSK acquisitions are available. We used for radargrammetric processing 2 CSK spotlight image pairs acquired at 1 day
of separation, in Same Side Viewing configuration, with baselines of 350 km. Furthermore, a dataset of 33 spotlight
images were selected to derive height measurements through both persistent scatterers interferometry(PSI) and
interferometric processing of 5 1-day separated pairs included in the dataset. We first predict how the errors in the
geometrical parameters and the correlation level between the images impact on the height accuracy. Then, two DEMs
were derived by processing the radargrammetric CSK pairs. According to the outcomes of the feasibility analysis,
processing parameters were chosen in order to guarantee nominal values of height accuracy within the HRTI Level 3
specifications. The products have a final resolution of 3 m. In order to assess the accuracy of these radargrammetric
DEMs, we used the height values provided by the PSI, and an interferometric DEM derived from the CSK tandem-like
In this work we explored a dataset made by more than 100 images acquired by COSMO-SkyMed (CSK) constellation
over the Port-au-Prince (Haiti) metropolitan and surrounding areas that were severely hit by the January 12th, 2010
earthquake. The images were acquired along ascending pass by all the four sensors of the constellation with a mean rate
of 1 acquisition/week. This consistent CSK dataset was fully exploited by using the Persistent Scatterer Interferometry
algorithm SPINUA with the aim of: i) providing a displacement map of the area; ii) assessing the use of CSK and PSI for
ground elevation measurements; iii) exploring the CSK satellite orbital tube in terms of both precision and size. In
particular, significant subsidence phenomena were detected affecting river deltas and coastal areas of the Port-au-Prince
and Carrefour region, as well as very slow slope movements and local ground instabilities. Ground elevation was also
measured on PS targets with resolution of 3m. The density of these measurable targets depends on the ground coverage,
and reaches values higher than 4000 PS/km<sup>2</sup> over urban areas, while it drops over vegetated areas or along slopes
affected by layover and shadow. Heights values were compared with LIDAR data at 1m of resolution collected soon
after the 2010 earthquake. Furthermore, by using geocoding procedures and the precise LIDAR data as reference, the
orbital errors affecting CSK records were investigated. The results are in line with other recent studies.
The application of Persistent Scatterer Interferometry (PSI) to slope instability monitoring poses challenges related to the
complex kinematics of the phenomenon, as well as to the unfavourable settings of the area affected by landslides, often
occurring on sites of limited extension, characterized by steep topography and variable vegetation cover. New-generation
SAR sensors, such as TerraSAR-X (TSX) thanks to their higher spatial resolution, make PSI applications very promising
for monitoring areas with low density man-made. Nevertheless, the application of techniques still remains problematic or
impossible in rural and mountainous areas. This is the case, for instance, for the Municipality of Carlantino, in Southern
Italy. Both C-band medium resolution SAR data from ESA satellites, and X-band high resolution SAR data from the
TSX satellite, were processed through the PSI algorithm SPINUA. Despite the higher spatial density of PS from TSX,
the landslide body is lacking coherent targets, due to vegetation and variable land cover. To allow stability monitoring, a
network of six CRs was designed and deployed over the landslide test site. Twenty-six TSX stripmap images were
processed by using both PSI and an ad hoc procedure based on double-difference analysis of DInSAR phase values on
the CR pixels, constrained by the accurate CR height measurements provided by DGPS. Despite the residual noise due to
the sub-optimal CR network and the strong atmospheric signal, displacement estimation on the CRs allows to propagate
the PSI results downslope, proving the stability of the landslide area subjected to consolidation works.
The exploitation of a multi-temporal stack of SAR intensity images seems to provide satisfactory results in flood detection problems when different spectral signature in presence of inundation are observed. Moreover, the use of interferometric coherence information can further help in the discrimination process. Besides the remote sensing data, additional information can be used to improve flood detection. We propose a data fusion approach, based on Bayesian Networks (BNs) , to analyze an inundation event, involving the Bradano river in the Basilicata region, Italy. Time series of COSMO-SkyMed stripmap SAR images are available over the area. The following random variables have been considered in the BN scheme: F, that is a discrete variable, consisting of two states: flood and no flood; the n-dimensional <i>i</i> variable, obtained by the SAR intensity imagery; the m-dimensional γ variable, obtained by the InSAR coherence imagery; the shortest distance <i>d</i> of each pixel from river course. The proposed BN approach allows to independently evaluate the conditional probabilities <i>P</i>(<i>i</i>|<i>F</i>), <i>P</i>(γ|<i>F</i>) and <i>P</i>(<i>F</i>|<i>d</i>), and then to join them to infer the value <i>P</i>(<i>F</i> = <i>flood</i>|<i>i</i>, γ, <i>d</i>), obtaining the probabilistic flood maps (PFMs). We evaluate these PFMs through comparisons with reference flood maps, obtaining overall accuracies higher than 90%.
Multi-Chromatic Analysis (MCA) of SAR images relays on exploring sub-band images obtained by processing portions
of range spectrum located at different frequency positions. It has been applied to interferometric pairs for phase
uwrapping and height computation. This work investigates two promising applications: the comparison between the
frequency-persistent scatterers (PS<sub>fd</sub>) and the temporal-persistent scatterers (PS), and the use of inter-band coherence of a single SAR image for vessel detection. The MCA technique introduces the concept of frequency-stable targets, i.e.
objects exhibiting stable radar returns across the frequency domain which is complementary to that of temporal stability
at the base of PS interferometry. Both spotlight and stripmap TerraSAR-X images acquired on the Venice Lagoon have
been processed to identify PS<sub>fd</sub> and PS. Different populations have been analyzed to evaluate the respective
characteristics and the physical nature of PS<sub>fd</sub> and PS. Concerning the spectral coherence, it is derived by computing the
coherence between sub-images of a single SAR acquisition. In the presence of a random distribution of surface
scatterers, spectral coherence must be proportional to sub-band intersection of sub-images. This model is fully verified
when observing measured spectral coherence on open see areas. If scatterers distribution departs from this distribution,
as for manmade structures, spectral coherence is preserved. We investigated the spectral coherence to perform vessel
detection on sea background by using spotlight images acquired on Venice Lagoon. Sea background tends to lead to very
low spectral coherence while this latter is preserved on the targeted vessels, even for very small ones. A first analysis
shows that all vessels observable in intensity images are easily detected in the spectral coherence images which can be
used as a complementary information channel to constrain vessel detection.
Classical applications of the MTInSAR techniques have been carried out in the past on medium resolution data acquired
by the ERS, Envisat (ENV) and Radarsat sensors. The new generation of high-resolution X-Band SAR sensors, such as
TerraSAR-X (TSX) and the COSMO-SkyMed (CSK) constellation allows acquiring data with spatial resolution
reaching metric/submetric values. Thanks to the finer spatial resolution with respect to C-band data, X-band InSAR
applications result very promising for monitoring single man-made structures (buildings, bridges, railways and
highways), as well as landslides. This is particularly relevant where C-band data show low density of coherent
scatterers. Moreover, thanks again to the higher resolution, it is possible to infer reliable estimates of the displacement
rates with a number of SAR scenes significantly lower than in C-band within the same time span or by using more
images acquired in a narrower time span. We present examples of the application of a Persistent Scatterers
Interferometry technique, namely the SPINUA algorithm, to data acquired by ENV, TSX and CSK on selected number
of sites. Different cases are considered concerning monitoring of both instable slopes and infrastructure. Results are
compared and commented with particular attention paid to the advantages provided by the new generation of X-band
high resolution space-borne SAR sensors.
The recent availability of wide-bandwidth, high-frequency, high-resolution SAR data is contributing to improved monitoring
capabilities of spaceborne remote sensing instruments. In particular, the new COSMO/SkyMed (CSK) and TerraSAR-
X (TSX) X-band sensors allow better performances in multitemporal DInSAR and PSI applications than legacy
C-band sensors such as ENVISAT ASAR, with respect to both target detection and terrain displacement monitoring
capabilities. In this paper we investigate about the possibility of achieving performances of PSI displacement detection
comparable to those of C-band sensors, by use of reduced numbers of high-resolution X-band acquisitions. To this end,
we develop a simple model for phase and displacement rate measurement accuracies taking into account both target
characteristics and sensors acquisition schedule. The model predicts that the generally better resolution and repeat-time
characteristics of new-generation X-band sensors allow reaching accuracies comparable to C-band data with a significantly
smaller number of X-band acquisitions, provided that the total time span of the acquisitions is large enough. This
allows in principle to contain the costs of monitoring campaigns, by using less scenes. Indications are more variable in
the case of short-time acquisition schedules, such as those involved in the generation of so-called "rush products" for
emergency applications. In this case, the higher uncertainty given by shorter total time spans lowers X-band performances
to levels mostly comparable to those of the legacy medium-resolution C-band sensors, so that no significant
gain in image number budget are foreseen. These theoretical results are confirmed by comparison of three PSI datasets,
acquired by ENVISAT ASAR, CSK and TSX sensors over Assisi (central Italy) and Venice.
The TerraSAR-X (copyright) mission, launched in 2007, carries a new X-band Synthetic Aperture Radar (SAR) sensor
optimally suited for SAR interferometry (InSAR), thus allowing very promising application of InSAR techniques
for the risk assessment on areas with hydrogeological instability and especially for multi-temporal analysis, such
as Persistent Scatterer Interferometry (PSI) techniques, originally developed at Politecnico di Milano. The
SPINUA (Stable Point INterferometry over Unurbanised Areas) technique is a PSI processing methodology
which has originally been developed with the aim of detection and monitoring of coherent PS targets in non
or scarcely-urbanized areas. The main goal of the present work is to describe successful applications of the
SPINUA PSI technique in processing X-band data. Venice has been selected as test site since it is in favorable
settings for PSI investigations (urban area containing many potential coherent targets such as buildings) and
in view of the availability of a long temporal series of TerraSAR-X stripmap acquisitions (27 scenes in all).
The Venice Lagoon is affected by land sinking phenomena, whose origins are both natural and man-induced.
The subsidence of Venice has been intensively studied for decades by determining land displacements through
traditional monitoring techniques (leveling and GPS) and, recently, by processing stacks of ERS/ENVISAT SAR
data. The present work is focused on an independent assessment of application of PSI techniques to TerraSAR-X
stripmap data for monitoring the stability of the Venice area. Thanks to its orbital repeat cycle of only 11 days,
less than a third of ERS/ENVISAT C-band missions, the maximum displacement rate that can be unambiguously
detected along the Line-of-Sight (LOS) with TerraSAR-X SAR data through PSI techniques is expected to be
about twice the corresponding value of ESA C-band missions, being directly proportional to the sensor wavelength
and inversely proportional to the revisit time. When monitoring displacement phenomena which are known to
be within the C-band rate limits, the increased repeat cycle of TerraSAR-X offers the opportunity to decimate
the stack of TerraSAR-X data, e.g. by doubling the temporal baseline between subsequent acquisitions. This
strategy can be adopted for reducing both economic and computational processing costs. In the present work,
the displacement rate maps obtained through SPINUA with and without decimation of the number of Single
Look Complex (SLC) acquisitions are compared. In particular, it is shown that with high spatial resolution SAR
data, reliable displacement maps could be estimated through PSI techniques with a number of SLCs much lower
than in C-band.
Image alignment is without doubt the most crucial step in SAR Interferometry. Interferogram formation requires
images to be coregistered with an accuracy of better than 1/8 pixel to avoid significant loss of phase coherence.
Conventional interferometric precise coregistration methods for
full-resolution SAR data (Single-Look Complex
imagery, or SLC) are based on the cross-correlation of the SLC data, either in the original complex form or
as squared amplitudes. Offset vectors in slant range and azimuth directions are computed on a large number
of windows, according to the estimated correlation peaks. Then, a two-dimensional polynomial of a certain
degree is usually chosen as warp function and the polynomial parameters are estimated through LMS fit from
the shifts measured on the image windows. In case of rough topography and long baselines, the polynomial
approximation for the warp function becomes inaccurate, leading to local misregistrations. Moreover, these
effects increase with the spatial resolution and then with the sampling frequency of the sensor, as first results on
TerraSAR-X interferometry confirm. An improved, DEM-assisted image coregistration procedure can be adopted
for providing higher-order prediction of the offset vectors. Instead of estimating the shifts on a limited number of
patches and using a polynomial approximation for the transformation, this approach computes pixel by pixel the
correspondence between master and slave by using the orbital data and a reference DEM. This study assesses the
performance of this approach with respect to the standard procedure. In particular, both analytical relationships
and simulations will evaluate the impact of the finite vertical accuracy of the DEM on the final coregistration
precision for different radar postings and relative positions of satellites. The two approaches are compared by
processing real data at different carrier frequencies and using the interferometric coherence as quality figure.
Many applications of SAR interferometry and differential interferometry lead to a set of sparse phase measurements that usually have to be unwrapped, and then interpolated on a regular grid. We investigate the utility of the scaling information available on the absolute phase, in the process of unwrapping a set of sparse, wrapped phase measurements. Scaling information is an important tool for the description of natural processes exhibiting fractal-like behaviour. One notable example is the interferometric phase contribution due to tropospheric inhomogeneities. Scaling properties can be estimated experimentally on a set of points through computation of the variogram. If it can be assumed that the absolute phase field obeys a defined scaling power law, then the wrapping operator will cause the variogram to depart from the modelled behaviour. Under these hypotheses, the difference between actual and modelled variogram can be used as an optimization Hamiltonian. In this work, we investigate whether the scaling information can be used as a constraint in retrieving the absolute (i.e. unwrapped) phase field from a set of sparse measurements. In particular, we consider here the problem of constructing a cost function which embodies the scaling requirement, and we test several strategies to optimise the cost.
In recent years it has been proved that combined analysis of SAR intensity and interferometric correlation images is a valuable tool in classification tasks where traditional techniques such as crisp thresholding schemes and classical maximum likelihood classifiers have been employed. In this work, developed in the framework of the ESA AO3-320 project titled Application of ERS data to landslide activity monitoring in southern Apennines, Italy, our goal is to investigate: (1) usefulness of SAR interferometric correlation information in mapping areas with diffuse erosional activity, including landslides; and (2) effectiveness of soft computing techniques in the combined analysis of SAR intensity and interferometric correlation images. Two neural classifiers are selected from the literature. The first classifier is a one- stage error-driven Multilayer Perceptron (MLP) and the second classifier is a Two-Stage Hybrid (TSH) learning system, consisting of a sequence of an unsupervised data-driven first stage with a supervised error-driven second stage. The TSH unsupervised first stage is implemented as either: (1) the on- line learning, dynamic-sizing, dynamic-linking Fully Self Organizing Simplified Adaptive Resonance Theory (FOSART) clustering model; (2) the batch-learning, static-sizing, no- linking Fuzzy Learning Vector Quantization (FLVQ) algorithm; or (3) the on-line learning, static-sizing, static-linking Self-Organizing Map (SOM). The input data set consists of three SAR ERS-1/ERS-2 tandem pair images depicting an area featuring slope instability phenomena in the Campanian Apennines of Southern Italy. From each tandem pair, four pixel-based features are extracted: the backscattering mean intensity, the interferometric coherence, the backscattering intensity texture and the backscattering intensity change. Our classification task is focused on the discrimination of land cover types useful for hazard evaluation, i.e., evaluation of areas affected by erosion. Classification results show that class erosion can be discriminated from other land cover classes when SAR mean intensity images are combined with coherence and texture information. In addition, our results demonstrate that soft computing techniques provide useful tools for the combined analysis of SAR intensity and coherence images. In particular, the TSH classifier employing the FOSART clustering algorithm shows: (1) an overall accuracy comparable with that of the other classification schemes under testing; (2) a training cost significantly lower than that of MLP and lower than that of TSH employing either FLVQ or SOM as its first stage; and (3) a capability of discriminating class erosion superior to that of the other classification schemes under testing.
In the last years, both local and global analysis techniques for the effective processing of interferometric SAR data have been proposed. We developed two local approaches to eliminate inconsistencies in the measured (wrapped) phase field, based on the local configurations of phase gradients in finite windows. The first technique adopts a fixed search strategy which 'cures' isolated residue couples by an appropriate series of corrections determined a priori. A second strategy uses the generalization capabilities of a neural network, trained on a suitable number of simulated target phase fields, to add 2 - (pi) cycles to the proper locations of the interferogram. These approaches, in spite of the high dimensionality of this problem, are able to correctly remove more than half the original number of pointlike inconsistencies on real noisy interferograms. This stems from the observation that phase unwrapping is an ill-posed problem, which has to be solved globally. Hence, a global stochastic method has been implemented, based on the minimization of a functional measuring the regularity of the phase field. The optimization tool used is simulated annealing with constraints. This methodology gives excellent results also in difficult conditions. We will present some of the recent results which aim at integrating the above-mentioned methodologies into powerful processing chains optimized for operating on large IFSAR datasets from real scenes. The effectiveness of such phase retrieving methods allows the application of sophisticated and innovative remote sensing techniques, such as differential interferometry.
2D phase unwrapping, a problem common to signal processing, optics, and interferometric radar topographic applications, consists in retrieving an absolute phase field from principal, noisy measurements. In this paper, we analyze the application of neural networks to this complex mathematical problem, formulating it as a learning-by-examples strategy, by training a multilayer perceptron to associate a proper correction pattern to the principal phase gradient configuration on local window. In spite of the high dimensionality of this problem the proposed MLP, trained on examples from simulated phase surfaces, shows to be able to correctly remove more than half the original number of pointlike inconsistencies on real noisy interferograms. Better efficiencies could be achieved by enlarging the processing window size, so as to exploit a greater amount of information. By pushing further this change of perspective, one passes from a local to a global point of view; problems of this kind are more effectively solved, rather than through learning strategies, by minimization procedures, for which we prose a powerful algorithm, based on a stochastic approach.
Phase unwrapping is one of the toughest problems in interferometric SAR processing. The main difficulties arise from the presence of point-like error sources, called residues, which occur mainly in close couples due to phase noise. We present an assessment of a local approach to the resolution of these problems by means of a neural network. Using a multi-layer perceptron, trained with the back- propagation scheme on a series of simulated phase images, fashion the best pairing strategies for close residue couples. Results show that god efficiencies and accuracies can have been obtained, provided a sufficient number of training examples are supplied. Results show that good efficiencies and accuracies can be obtained, provided a sufficient number of training examples are supplied. The technique is tested also on real SAR ERS-1/2 tandem interferometric images of the Matera test site, showing a good reduction of the residue density. The better results obtained by use of the neural network as far as local criteria are adopted appear justified given the probabilistic nature of the noise process on SAR interferometric phase fields and allows to outline a specifically tailored implementation of the neural network approach as a very fast pre-processing step intended to decrease the residue density and give sufficiently clean images to be processed further by more conventional techniques.
SAR interferometry can be used to derive topographic information (DEM) on the earth surface. An operation called geocoding is necessary to translate the DEM form a range- azimuth to a latitude-longitude reference system. This paper introduces a new algorithm for geocoding interferometric DEMs based on an iterative procedure using the reference ellipsoid as the first guess, then locating each earth point on a succession of planes locally parallel to the ellipsoid. The procedure is shown to geometrically converge to the considered DEM point. Given its high accuracy, this method could be used as a precision tool for use in difficult zones.
Coordinates in the 3D space of elements in a SAR image can be obtained by the combination of along-track, slant-range and interferometric fringe measurements. In order to evaluate the elevation of a pixel with respect to a slant- range reference plane, its absolute interferometric phase is required and this is typically derived unwrapping a 2D interferometric fringe pattern. Phase inconsistencies in SAR interferograms due to noise and topography determine unwrapping errors which appear as discontinuities in the computed absolute phase field. Phase aliasing arising from rapid phase variations from topography generates 2D unwrapping inconsistencies characterized by phase patterns statistically different from those induced by noise. In this paper, the spatial configurations of the phase field around residues is utilized in the phase unwrapping procedure. The feasibility of a neural network approach for classifying residual complex geometric phase patterns requiring different corrective measures is also presented. In addition, a method based on pseudo-differential interferometry to resolve residual inconsistencies as noise- or topography-generated is explored.