MULTID FOCUSING FOR ACCURATE TARGET LOCATION AND TRACKING OF SLOW MOVEMENTS: RESULTS AND VALIDATION

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1 MULTID FOCUSING FOR ACCURATE TARGET LOCATION AND TRACKING OF SLOW MOVEMENTS: RESULTS AND VALIDATION F. Serafino 1, G. Fornaro 1, A. Pauciullo 1, F. Lombardini 2, M. Costantini 3 1 IREA-CNR via Diocleziano Napoli Italy Fax (serafino.f, pauciullo.a, fornaro.g)@irea.cnr.it 2 Dipartimento di Ingegneria della Informazione, Università di Pisa Via G. Caruso, Pisa (Italy) Fax f.lombardini@iet.unipi.it 3 Telespazio S.p.A Via Tiburtina Roma (Italy) mario.costantini@telespazio.com Abstract Multi-Dimensional (MultiD) SAR imaging is a coherent data combination technique aimed to space (3D) and space-velocity (4D) focusing. It extends the concept of SAR interferometry and Differential SAR Interferometry and gives new options for the analysis and monitoring of ground scenes at high resolution. We present the results obtained by processing ERS real data. Atmospheric and low resolution deformation monitoring, used for the phase calibration of high resolution data, are obtained via the Enhanced Spatial Differences (ESD) technique. Ongoing work on 3D and 4D performances is also described. 1. INTRODUCTION In Earth Observation, unique capabilities are associated with the use of remote sensing via Synthetic Aperture Radars (SAR) and, particularly, with the extensions of SAR to interferometric modes and more generally to the joint use of coherent multiple acquisitions: the latter is a leading-edge research area usually referred to as coherent multichannel SAR data processing. By exploiting the phase interference between two (SAR Interferometry) or more (multibaseline SAR interferometry) views, accurate DEM can be generated. Furthermore, acquiring images at different time intervals (multitemporal or multipass SAR) precise tracking of the velocity of ground deformations at on accuracy of the order of mm/yr can be achieved [1]. Standard techniques essentially use only the phase information contained in the data, i.e.., they neglect the amplitude information. Moreover, to properly work, they assume the scattering mechanism to be of basic nature: i.e., dominated by a permanent (or better persistent) scatterer or distributed on the ground surface portion associated to the pixel. When the radiation penetrates under the surface, a situation that rarely occurs with existing ERS and ENVISAT sensors, operating at relatively high frequencies, or ground topography is as steep to generate critical projection of the scatterers in the slant imaging geometry (layover), or there is the presence of a high spatial density of strong scatterers, the signal received in a generic pixel may contain the superposition of responses coming from multiple scatterers. The latter two conditions are frequent when data are acquired over complex scenarios with an irregular surface profile such as urban areas or large infrastructures. Precise target height estimation, and thus precise target location, as well as the imization of the number of tracked scatterers are issues of primary importance, especially when imaging dense urban areas. SAR Tomography [2] is a way of overcoming limitations of standard interferometric based algorithms for target height determination by achieving fully 3D focused images. Differential SAR Tomography [3], i.e. the extension of 3D to 4D (space/velocity) SAR imaging not only makes proper use of amplitude information in dominant target monitoring but also allows separating and estimating possible relative deformations of targets interfering in the same resolution cell. Proc. Envisat Symposium 2007, Montreux, Switzerland April 2007 (ESA SP-636, July 2007)

2 2. BASICS OF MULTI-DIMENSIONAL IMAGING Let us refer to a SAR system acquiring data at temporal instants t n, n=1,,n and with a distribution along the elevation (baselines) b n n=1,,n referred to a master acquisition. Let us also assume the presence of a ground displacement, whose velocity component in the line of sight is equal to v. After accommodation of geometric phase factors and compensation of propagation delay due to the presence of the atmosphere (see the following section) the received signal at the n-th antenna in a given azimuth and range pixel at full resolution may be expressed as [4]: Core of the ESD, sketched in Figure 1 is the Spatial Differences (SD) step that allows achieving a quick preestimate of the Mean Deformation Velocity (MDV) and Residual Topography (RT) in a single and very simple step. The SD algorithm inherently provides APS mitigation via an analysis aimed at estimating the gradient of MDV and RT, and which involves a spatial filtering implemented by means of a spatial differentiation operation. SD step allows achieving only measures of the mean temporal evolution whereas no information is given time by time: this is necessary for the estimation of APS. g n = s s v v ( s, v) j2πξ n s+ j2πη v n γ e dvds (1) 2tn ζ n = 2b n ( λr) ηn = (2) λ where γ is the reflectivity 2D space-velocity distribution. 3D imaging can be considered as a particular case where γ ( s, v) = γ ( s) δ ( v), δ () v being the Dirac generalized function. Equation (1) shows that, but for proper phase-calibration pre-processing, the received data at different antennas correspond to the samples of the 2D Fourier Transform (FT) of the SV reflectivity density function at the frequencies described by (2). Our problem is to reconstruct the object function γ, that is to achieve a profiling of the scattering along the elevation and velocity, to identify dominant targets and separate interfering scatterers located at different elevation. It basically consists of an inversion of a linear problem with discrete data. Different techniques can be used, the simplest one being Beamforming, whereas another can be related to adaptive Beamforming [3]. In the processing of real data considered in this work we used a regularized approach based on the Singular Value Decomposition technique that shows advantages related to the robustness, simplicity, and provides also the possibility to directly work with full resolution data, see [2] for further details. 3. THE ESD TECHNIQUE To estimate the Atmospheric Phase Screen (APS) we used a low resolution Multipass DInSAR processing technique named Enhanced Spatial Differences (ESD). It allows separating APS and deformation components from a stack of interferograms formed by interferometric beatings of data acquired over multiple passes. ESD has been demonstrated [5] [6] to be a powerful technique for monitoring deformation at low resolution (small scale) over wide areas. Figure 1. Structure of the ESD technique To generate deformation time series, i.e., to track also possible non-linear time evolutions, the interferometric information must be accessed layer by layer on the multipass stack. To this end interferograms are spatially unwrapped: we use a procedure that enhances the solution starting form that achieved via the SD analysis, that conversely carries out a multibaseline unwrapping, by means of a phase subtraction and addition back. A system of linear equations between M knowns - the unwrapped interferometric phase - and N unknowns - the absolute phase values ϕ n ( i, j) for each acquisition - may be then written and inverted. APS is the result of a final spatial-temporal filtering on the resulting time series. 4. PERFORMANCE PREDICTION Work is in progress on developing analytical tools for performance prediction and characterization of precision limits in 3D Tomography and 4D Differential Tomography imaging, by extending the derivation of the

3 Cramér Rao Lower Bound (CRLB) for multibaseline estimation of multiple speckled signal components in [7][8]. A first extension for 3D Tomography accounts for residual phase miscalibration errors through the Hybrid CRLB tool, for the low resolution case [9]. A sample result is reported in Figure 2 for the used 58 tracks of next section, speckled signal from an elevationcompact source (baseline to critical baseline ratio 0.05), 10 looks, 1 mm standard deviation of residual atmospheric one-way path delay variations (0.02 in wavelength units) uncorrelated from track to track, compensated LOS velocity; the solid curve is the reference CRLB for no miscalibration. Derivation of CRLBs for 4D Differential Tomography has also started for the low resolution case in [10] also accounting for temporal decorrelation from a sample internal Brownian motion model [11][12], tackling both single and multiple acquisitions for each pass i.e. including the cooperative satellite formations case, or for the full resolution case. Such performance prediction tools, when further integrated together, may be used for judging estimator efficiency, characterizing potentials of the 3D and 4D imaging techniques, and as guidelines for designing multibaseline and multitemporal acquisitions patterns and systems. It is also worth noting that in the Differential Tomography framework, recent results give indication that impact of some temporal decorrelation sources on the elevation precision may be reduced (low resolution case) [13], as hinted in [3]. measures (stars) and the leveling network (squares) in the Campi Flegrei area. After compensation of APS, MultiD focusing is carried out. As far as 3D focusing and separation of different scatterers in height is concerned, the reader may refer to [14] where results for the same passes have shown the capability of the technique to separate double scatterers interfering within the same resolution cell. Figure 3. ESD Mean Deformation Velocity saturated in ±6mm/yr superimposed to a Landsat image ( Figure 2. Sample result of Hybrid Cramér Rao Lower Bound for 3D Tomography 5. RESULTS In our study we have used 58 images acquired over descending orbits track 36 - frame 2781 in the Campania area (temporal span is of about 10 years from 1992 to 2001). The MDV map for the whole frame is shown in Figure 3 whereas a particular of the deformations occurring in the urban area of Napoli is given in Figure 4. Measured radar deformations have been validated with classical geodetic data: see Figure 5 for the comparison between the vertical deformation radar Figure 4. Napoli Right: particular of the urban area of For what concerns 4D focusing, the processing has been carried out to achieve the profiling of the scattering distribution in the elevation-velocity domain. Subsequently data have been further processed to identify single (dominant) and double (dominant-weak pairs) scatterers via an automatic procedure that analysis

4 of the elevation-velocity pattern. Figure 6 shows the result of the full resolution analysis implemented, by the use, over a patch relative to the Campi Flegrei-Pozzuoli caldera at the West side of Napoli for single scatterers. In Figure 7 it is plotted the total large scale deformation time series for a pixel in the caldera. In Figure 8 it is shown the dominant scatterers distribution for the industrial area at the Eastern part of Napoli. Finally Figure 9 shows the results of 4D double scatterers separation (right) compared to the distribution of single scatterers (left). Double scatters lays at the same range and are located at different elevation; accordingly after geocoding the will appear as ground scatterers pairs aligned along the ground range direction that, for descending passes, points to West and is slightly rotated towards Nord. From Figure 9 it is evident the capability of 4D imaging to single out contributions of weaker scatterers (smaller full circles) from dominant scatters (larger full circles) Figure 5. Comparison between the ESD vertical radar deformation time series (stars) and leveling measures (squares). 6. CONCLUSIONS We have presented results of the multi-dimensional SAR imaging technique. They clearly show that such technique may be a valid alternative to the traditional Permanent Scatterers approach to monitor deformations at high resolution on dominant targets. Moreover, it allows extending the concept of Permanent Scatterers to separate multiple targets interfering in the same resolution cell. In particular, although current satellite technology provides only a single acquisition per time (i.e., multibaseline data are collected at different times, or conversely, only a single sample in the 2D spacevelocity spectral domain is present at each pass) we have shown that: a) 3D imaging can be performed and thus the separation of different scatterers interfering in the same resolution cell is possible, b) 4D imaging can be performed and thus the separation of different scatterers interfering in the same resolution cell as well as the estimation of their relative velocities is also possible. 7. ACKNOWLEDGMENTS The authors wish to thank the INGV-OV for providing the access to the raw data used in this study and the CRdC-AMRA for the use of the data processing-cluster. The authors are also grateful to Paolo Berardino and Giovanni Zeni for the support in data geocoding and GIS data integration, and to Gianni Ricciardi (INGV- OV) for providing the leveling measurements in the Campi Flegrei area. Special thanks are given to Dr. Matteo Pardini from University of Pisa, Department of Information Engineering, for his help with the Hybrid CRLB calculation. This work has been partially supported in the framework of the LIMES project, which has received research funding from the European Commission under FP SPACE-1/GMES SECURITY. 8. REFERENCES [1] A. Ferretti,, C. Prati, and F. Rocca, 2000, Nonlinear subsidence rate estimation using permanent scatterers in differential SAR interferometry, IEEE Trans. Geosci. and Remote Sens., vol. 38 (5), pp , [2] G. Fornaro, F. Serafino, and F. Lombardini: 3D Multipass SAR Focusing: Experiments with Long-Term Spaceborne Data, IEEE Trans. Geosci. and Remote Sens., vol.43, pp , April [3] F. Lombardini, Differential Tomography: a New Framework for SAR Interferometry, IEEE Trans. Geosci. and Remote Sens., vol.43, pp , [4] G. Fornaro, F. Lombardini, F. Serafino, Multidimensional imaging with ERS data, Fringe 2005 Workshop, Frascati (Italy), 28 November 2 December 2005, ( programme.html). [5] G. Fornaro, A. Pauciullo and F. Serafino, Deformation Monitoring over large areas with Multipass Differential SAR Interferometry: a new approach based on the use of Spatial Differences, submitted to International Journal of Remote Sensing, January [6] G. Fornaro, A. Pauciullo and F. Serafino, Multipass SAR Processing for Urbanized Areas Imaging and Deformation Monitoring at Small and Large Scales, Proceeding of the IEEE Urban 2007 Conference, Paris [7] F. Gini, F. Lombardini, M. Montanari, Layover Solution in Multibaseline SAR Interferometry, IEEE Trans. on Aerospace and Electronic Systems, Vol. 38, No. 4, October 2002, pp [8] F. Lombardini, M. Montanari, F. Gini, Reflectivity Estimation for Multibaseline Interferometric Radar Imaging of Layover Extended Sources, IEEE Trans. on Signal Processing, Vol. 51, No. 6, June 2003, pp [9] M. Pardini, F. Lombardini, F. Gini, The Hybrid Cramér-Rao Bound for DOA Estimation of Extended Sources in Presence of Array Errors, submitted to IEEE Trans. on Signal Processing, Nov [10] F. Lombardini, M. Pardini, Sample Results on New Views on Temporal Decorrelation Effects, Internal Reports, University of Pisa, Dec. 2006, April [11] F. Lombardini, H.D. Griffiths, "Effect of Temporal Decorrelation on 3D SAR Imaging using Multiple Pass Beamforming," Proc. IEE-EUREL Meeting on Radar and Sonar Signal Processing, pp.34/1-34/4, Peebles, UK, July [12] F. Rocca, F. De Zan, A. Monti Guarnieri, S. Tebaldini, "Advantages of Sentinel-1 s Single Mode Short Revisit Time: Persistent Scatterer Quality Results with Distributed Scatterers," ESA ENVISAT Symposium, Montreux, Switzerland, April [13] F. Lombardini, New Potentials of Differential Tomography Robust DEM Generation, Internal Report, University of Pisa, April To be presented at IGARSS [14] G. Fornaro, and F. Serafino, Imaging Single and Double Scatterers in Urban Areas via SAR Tomography, IEEE Trans. Geosci. and Remote Sens, vol. 44 (12), pp , 2006.

5 Figure 6. Large scale results obtained via the use of the 4D (Differential Tomography) procedure: highlight on the dominant scatterers in the Campi Flegrei area close to Napoli overlaid to an ortophoto. Figure 7. Large scale deformation time series related to a dominant target with the high deformation rate located on the Campi Flegrei Caldera in the area.

6 Figure 8. Results obtained via the use of the 4D technique: highlight on dominant targets in the industrial area at East of Napoli overlaid to an ortophoto. Figure 9. Separation between scatterers interfering in the same azimuth-range pixel in the area of Vomero overlaid to an ortophoto. Left: dominant scatterers; right: double scatterers.