Ishak Daoud1, Assia Kourgli2, Aichouche Belhadj Aissa2.
1Telecommunications and information processing, Faculty of Electronics and Computer Science, Laboratory of Image processing and radiation, University of Science and Technology Houari Boumediene, Algiers.
2Laboratory of Image processing and radiation, University of Science and Technology Houari Boumediene, Algiers, Algeria
The SAR tomography, is anapproach that uses multi-pass SAR images to decompose the target basing on its backscatter mechanisms, this decomposition helps to generate the reflectivity profile on the elevation axis. However, the common Rayleigh resolution related to the Nyquist condition, can cause quality problems in elevation, due to the low number of acquisitions, the non-regular distribution of the baseline and its small aperture in orthogonal baseline. The work we have presented in this paper, concerns the reconstruction of the reflectivity profile of TerraSAR-x radar target images by exploiting the concept of CS ‘Compression Sensing’, assuming that the target has generally a spars representation along the elevation direction. We have presented also, a reconstruction for some simulated profiles based on the radar characteristics of TERRASR-X, using some algorithms asBasis Pursuit‘BP’ and Basis Pursuit Denoised ‘BPDN’, to well Simulated a real reconstruction when the measurements are noised. Based on the results obtained by the convex reconstruction algorithmsimplemented on MATLAB, we have shown how the number of measures necessary for reconstruction can be reduced and how reconstructed samples can be increased, which can bring Better resolution in elevation for anoisy measurement, based on a lemma that binds the sparsity, the total reconstruct samples and the number of measures.
SAR Tomography Compressive sensing, sparsity, L1&L0 norm-minimization, SAR tomography, Restricted Isometry Property.