Three-Dimensional SAR Focusing From Multipass Signals Using Compressive Sampling
By Alessandra Budillon, Annarita Evangelista, Gilda Schirinzi. IEEE Transactions on Geoscience and Remote Sensing, 2011
315 citations recorded by the source corpus.
Three-dimensional synthetic aperture radar (SAR) image formation provides the scene reflectivity estimation along azimuth, range, and elevation coordinates. It is based on multipass SAR data obtained usually by nonuniformly spaced acquisition orbits. A common 3-D SAR focusing approach is Fourier-based SAR tomography, but this technique brings about image quality problems because of the low number of acquisitions and their not regular spacing. Moreover, attained resolution in elevation is limited by the overall acquisitions baseline extent. In this paper, a novel 3-D SAR data imaging based on Compressive Sampling theory is presented. It is shown that since the image to be focused has usually a sparse representation along the elevation direction (i.e., only few scatterers with different elevation are present in the same range-azimuth resolution cell), it suffices to have a small number of measurements to construct the 3-D image. Furthermore, the method allows super-resolution imaging, overcoming the limitation imposed by the overall baseline span. Tomographic imaging is performed by solving an optimization problem which enforces sparsity through ℓ1-norm minimization. Numerical results on simulated and real data validate the method and have been compared with the truncated singular value decomposition technique.
Subfields: Aerospace Engineering, Biomedical Engineering, Computational Mechanics
View Paper: https://www.semanticscholar.org/paper/e10dc35413a3f90ab15580d2cf640c8727f0bc8f
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In Aerospace Engineering
- Paolo Rocca
- Andrea Massa
- Giacomo Oliveri
- Robert J. Mailloux
- Benjamin Fuchs
- Luca Manica
- Z. Nie
- Qing Huo Liu
In Biomedical Engineering
In Computational Mechanics
Citations in the corpus
- Compressive Sensing in Electromagnetics - A Review (2015, methodology): “Different greedy/local minimization solvers have been employed, ranging from BP [33, 90], BP DeQuantizer [88, 91], Douglas–Rachford splitting method [89, 92], up to the iterative shrinkage/thresholding method [89, 93].”