Persistent Scatterer Densification Through Capon-Based SAR Reprocessing for Sentinel-1 TOPS Data

Abstract

Several researchers have shown that the Capon algorithm can be applied to reprocess SAR images, resulting in super-resolution reconstructed scenes with lower sidelobe levels. Thus by employing the Capon-based reprocessed images in Persistent Scatterer Interferometry (PSI), the persistent scatterer (PS) density can be increased. In this letter, we exploit the Capon-based PS densification method for Sentinel-1 (S-1)Terrain Observation by Progressive Scans(TOPS) data. We propose a revised robust approach of the Capon algorithm, which applies the automatic diagonal loading (DL) method when the condition number of the covariance matrix is big enough. The proposed approach is robust and can avoid spurious persistent scatterer candidate (PSC) points introduced by DL approaches. We also consider and analyze the spectral property caused by the scanning mode of TOPS in the reprocessing. We applied the revised-robust-Capon-based reprocessing algorithm to a stack of real-life S-1 data and selected PSCs from them. The final result shows that the number of PSs increases by approximately 30% with respect to the original stack.