HomoR-CS: A homogeneous region-based compressed sensing method for SAR tomography

Abstract

The performance of most existing tomographic synthetic aperture radar (SAR) (TomoSAR) methods that reconstruct the scene pixel-by-pixel is degraded by speckle noise and low signal-to-noise ratio. To solve these problems, we propose a homogeneous region-based compressed sensing (HomoR-CS) method for SAR tomography. This method enhances the processing approach by transitioning from the traditional pixel-by-pixel processing to the joint processing based on multiple adjacent pixels in homogeneous regions. First, a similarity measure is designed to cluster pixels that have similar electromagnetic scattering intensities and three-dimensional (3-D) positions into homogeneous regions. Then, the homogeneous region-oriented TomoSAR signal model is proposed and the solution is given. Sparse approximation at the homogeneous region level is used to estimate the elevation for every pixel. The multiple adjacent pixels within the homogeneous region are jointly processed to determine the initial elevation centers, which are used as the prior information for the subsequent accurate elevation estimation of each pixel. Finally, the experimental analyses using both simulated and measured data validate the effectiveness of the HomoR-CS method. The results show that the HomoR-CS method reduces the outliers of 3-D reconstruction results and enhances the recovery of weak scattering targets.

Keywords

• 3-D reconstruction
• TomoSAR
• Compressed sensing
• Homogeneous region
• Urban area