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SAR Tomography via Nonlinear Blind Scatterer Separation

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Document pages: 14 pages

Abstract: Layover separation has been fundamental to many synthetic aperture radarapplications, such as building reconstruction and biomass estimation.Retrieving the scattering profile along the mixed dimension (elevation) istypically solved by inversion of the SAR imaging model, a process known as SARtomography. This paper proposes a nonlinear blind scatterer separation methodto retrieve the phase centers of the layovered scatterers, avoiding thecomputationally expensive tomographic inversion. We demonstrate thatconventional linear separation methods, e.g., principle component analysis(PCA), can only partially separate the scatterers under good conditions. Thesemethods produce systematic phase bias in the retrieved scatterers due to thenonorthogonality of the scatterers steering vectors, especially when theintensities of the sources are similar or the number of images is low. Theproposed method artificially increases the dimensionality of the data usingkernel PCA, hence mitigating the aforementioned limitations. In the processing,the proposed method sequentially deflates the covariance matrix using theestimate of the brightest scatterer from kernel PCA. Simulations demonstratethe superior performance of the proposed method over conventional PCA-basedmethods in various respects. Experiments using TerraSAR-X data show animprovement in height reconstruction accuracy by a factor of one to three,depending on the used number of looks.

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