Spatio-Temporal Filtering Approach for Tomographic SAR Data

Abstract

Synthetic aperture radar tomography (TomoSAR) has recently received particular interest from the remote-sensing community, due to its ability to provide 3-D reconstructions of environments with complex structures. Unfortunately, different forms of decorrelations and processing errors affect the quality of the resulting 2-D/3-D images. One way to cope with the impact of these nuisances is to apply appropriate filtering to the interferometric data stack as a preprocessing step. The first obstacle to be dealt with, especially in urban areas, is to define a filter whose parameters have to be set in such a way as to improve smoothing capabilities while preserving edges. To this aim, the main objective of this article is twofold: 1) the application of a spatio-temporal contextual filter whose parameters depend on 3-D quality indicators of the multibaseline interferometric image stack and 2) evaluation of the denoising effect on the application of nonparametric spectral estimation and detection algorithms. For that, we consider several quantitative metrics to assess, on the one hand, the filtering performances, and, on the other hand, its impact on the reflectivity function recovered from conventional tomographic inversion and detection methods. Experimental results from a set of TerraSAR-X (TSX) images highlight the efficiency of the filtering process by improving the scatterers’ detection and height localization, of a man-made structure.