SAR Tomography via Nonlinear Blind Scatterer Separation

TL;DR

This paper introduces a nonlinear blind scatterer separation technique using kernel PCA to improve SAR tomography, effectively retrieving scatterer phase centers without complex inversion, and demonstrating enhanced accuracy over traditional methods.

Contribution

It proposes a kernel PCA-based nonlinear method for scatterer separation in SAR tomography, avoiding computationally intensive inversion and addressing limitations of linear approaches.

Findings

Kernel PCA mitigates phase bias in scatterer separation.

The method improves height reconstruction accuracy by 1-3 times.

Simulations and TerraSAR-X data validate the method's effectiveness.

Abstract

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