Contextual Tomographic SAR Denoising Approach for Estimating Scatterers’ Height and Deformation Velocity

Abstract

The reliability of tomographic synthetic aperture radar (TomoSAR) products depends on the quality of complex-valued tomographic interferograms. The latter is unfortunately affected by noise from various sources. In order to reduce their impact and thus improve the outcome, filtering methods can be implemented at different levels of the TomoSAR process. In this article, we propose the application of a contextual denoising approach in transformed domains, based on subband decomposition and nonlinear weighting, with the aim to study its influence on TomoSAR height and deformation velocity estimation using generalized likelihood ratio test detection. Both spatial and spatiotemporal arrangements of overlapping blocks are considered in wavelet domains. The nonlinear filtering parameter is estimated from the coherence and/or pseudo-correlation (CPC) indicators. In order to show the effectiveness of the approach, the obtained findings have been compared to the state-of-the-art methods, namely, Goldstein and Baran filters. The assessment of the results with respect to denoising and TomoSAR evaluation metrics was carried out using both simulated and real data acquired by TerraSAR-X (TSX) satellite over the city of Naples.