CS and GLRT in SAR Tomography Compressive Sensing and Generalized Likelihood Ratio Test in SAR Tomography G. Fornaro 1,2 , A. Pauciullo 1 , D. Reale 1 , M. Weiss 3 , A. Budillon 2 , G. Schirinzi 2 1 - Institute for Electromagnetic Sensing of the Environment (IREA) National Research Council (CNR), Naples, Italy 2 - University of Napoli “Parthenope” Department of Engineering, Naples, Italy 3- Fraunhofer FHR Wachtberg, Germany.
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CS and GLRT in SAR Tomography
Compressive Sensing and Generalized LikelihoodRatio Test in SAR Tomography
G. Fornaro1,2, A. Pauciullo1, D. Reale1, M. Weiss3, A. Budillon2, G. Schirinzi2
1 - Institute for Electromagnetic Sensing of the Environment (IREA)
National Research Council (CNR), Naples, Italy
2 - University of Napoli “Parthenope”
Department of Engineering, Naples, Italy
3- Fraunhofer FHR
Wachtberg, Germany.
CS and GLRT in SAR Tomography 2
Outline
� Synthetic Aperture Radar (SAR) Tomography and motivation for super-resolution
� Compressive Sensing in Tomo-SAR
� Scatterer detection problem Tomo-SAR . Generalized Likelihood Ratio Test (GLRT) schemes: the GLRT with cancellation (GLRT-C) and the support based GLRT (sup-GLRT) approaches
� Application to real data: CS, GLRT-C vs sup-GLRT and sup-GLR vs CS
� Conclusions and future works
CS and GLRT in SAR Tomography 3
SAR Tomography for full 3D Imaging
� SAR Interferometry and Differential SAR Interferometry has important applicationsin Digital Elevation Model (DEM) reconstruction and monitoring of deformation.
� SAR Tomography extends interferometric approaches for application to complexscenarios.
� By synthesizing an antenna also in the slant height direction (orthogonal tothe line of sight) it is possible to analyze the vertical structure of the scatteringthus extending SAR imaging form 2D (azimuth-slant range) to 3D (azimuth-slantrange-slant height).
CS and GLRT in SAR Tomography 4
3D SAR Imaging
( )∫−
−=max
max
2
s
s
sjn dsesx nπξγ ( ) Nnrbnn ,....12 == λξ
r
s FOURIER INVERSION FROM IRREGULAR SAMPLES:
� BeamForming (BF)
� Regularized inversion (SVD)
� Adaptive Beamforming (Capon)
� Compressive sensing (CS)1,2
signal to the n-th antenna
backscattering distribution
along the slant height
N acquisitions with spatial (orthogonal) baseline distribution Nbb ......1
( )Brs 2λ=∆
RAYLEIGH
RESOLUTION
minmax bbB −=
1. A. Budillon, A. Evangelista, G. Schirinzi. Three-Dimensional SAR Focusing From Multipass
Histogram of the difference between the height of double scatterers estimated by CS and sup-GLRT (mask of points detected by sup-GLRT)
CS and GLRT in SAR Tomography 29
Deformation Mean Velocity Difference between CS and sup-GLRT
Histogram of the difference between the deformation mean velocity of double scatterers estimated by CS and sup-GLRT (mask of points detected by sup-GLRT)
CS and GLRT in SAR Tomography 30
CS Post Detection Single Scatterers
CS and GLRT in SAR Tomography 31
Sup-GLRT Single Scatterers
CS and GLRT in SAR Tomography 32
CS Post Detected Double Scatterers(lower)
CS and GLRT in SAR Tomography 33
Sup-GLRT Double Scatterers (lower)
CS and GLRT in SAR Tomography 34
CS Post Detected Double Scatterers(higher)
CS and GLRT in SAR Tomography 35
Sup-GLRT Double Scatterers (higher)
CS and GLRT in SAR Tomography 36
Conclusions and future works
SAR Tomography allows implementing a radar scanner from the space to reconstruct 3D point clouds and monitor deformations.
Next generation VHR sensors (COSMO-SkyMED II Generation, HRWS) will allow further improving this technology for application to urban area and critical infrastructure monitoring.
Super-resolution in SAR tomography allows achieving improvements in the generation of 3D point clouds.
An open issue is the coupling between the reliability of the reconstruction and the computational performances. To this end a key point seems to be associated with the “assimilation” of proper detection schemes within computationally efficient L1 methods.