Polarimetric Characterization and Temporal Stability Analysis of Urban Target Scattering

This paper studies the polarimetric-dispersion properties of urban targets and their evolution along time in terms of the geometrical configuration. The relations between target geometry and the scattering behavior have been defined through the analysis of large stacks of simulated images. Scattering maps and synthetic aperture radar (SAR) images have been synthesized with the numerical tool GRaphical Electromagnetic COmputing SAR for different qualitative models of two real buildings. Ground-based SAR (GB-SAR) data acquired in a subsidence measurement campaign has been used to assess the simulator's realism. These data have permitted the identification of the critical simulation parameters and their range of recommended values for realistic simulations. In the context of very high resolution images, the results derived from this study may be crucial for making progress in urban-image postprocessing. As the different resolution cells comprise few scattering centers showing a quasi-deterministic scattering behavior, nonprobabilistic models based on target's geometry seem more suited for scattering modeling. In these models, the geometry-scattering (GS) links precisely inferred from simulated images can be very important. In addition to change detection and land classification, GS models may help in improving the interpretation of subsidence results with differential interferometry. Certainly, new processing algorithms can be developed exploiting the available scattering data with more physical sense. In addition, they can take more advantage of the fine resolution and polarimetric capabilities of the new sensors, like TerraSAR-X or RADARSAT-2.

[1]  D. Wehner High Resolution Radar , 1987 .

[2]  T. Thayaparan,et al.  Focusing distorted ISAR images using Adaptive Local Polynomial Fourier Transform , 2006, 2006 International Radar Symposium.

[3]  Maxim Neumann,et al.  A Self-Initializing PolInSAR Classifier Using Interferometric Phase Differences , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[4]  Jordi J. Mallorquí,et al.  On the Usage of GRECOSAR, an Orbital Polarimetric SAR Simulator of Complex Targets, to Vessel Classification Studies , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Gerard Margarit Martín Marine applications of SAR polarimetry , 2007 .

[6]  Carlos López-Martínez,et al.  Polarimetric Differential SAR Interferometry: First Results With Ground-Based Measurements , 2009, IEEE Geoscience and Remote Sensing Letters.

[7]  Carlos López-Martínez,et al.  Grecosar, a SAR simulator for complex targets: Application to urban environments. , 2007, 2007 IEEE International Geoscience and Remote Sensing Symposium.

[8]  Konstantinos P. Papathanassiou,et al.  Polarimetric SAR interferometry , 1998, IEEE Trans. Geosci. Remote. Sens..

[9]  Vito Alberga Volume decorrelation effects in polarimetric SAR interferometry , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[10]  Irena Hajnsek,et al.  Polarimetric and interferometric characterization of coherent scatterers in urban areas , 2006, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Carlos López-Martínez,et al.  Exploitation of Ship Scattering in Polarimetric SAR for an Improved Classification Under High Clutter Conditions , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Carlos López-Martínez,et al.  Atmospheric Artifact Compensation in Ground-Based DInSAR Applications , 2008, IEEE Geoscience and Remote Sensing Letters.

[13]  Laurent Ferro-Famil,et al.  Building characterization using L-band polarimetric interferometric SAR data , 2005, IEEE Geoscience and Remote Sensing Letters.

[14]  Jordi J. Mallorqui,et al.  Scattering-Based Model of the SAR Signatures of Complex Targets for Classification Applications , 2008 .

[15]  Carlos López-Martínez,et al.  Phenomenological Vessel Scattering Study Based on Simulated Inverse SAR Imagery , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[16]  E.J. Candes,et al.  An Introduction To Compressive Sampling , 2008, IEEE Signal Processing Magazine.

[17]  Jordi J. Mallorquí,et al.  Single-Pass Polarimetric SAR Interferometry for Vessel Classification , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[18]  Miguel Ferrando Bataller,et al.  GRECO: graphical electromagnetic computing for RCS prediction in real time , 1993 .

[19]  Juan M. Rius,et al.  High-frequency RCS of complex radar targets in real-time , 1993 .

[20]  Daniele Perissin,et al.  Urban-Target Recognition by Means of Repeated Spaceborne SAR Images , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[21]  Daniele Perissin,et al.  High-Accuracy Urban DEM Using Permanent Scatterers , 2006, IEEE Transactions on Geoscience and Remote Sensing.