Detection of homogeneous objects in multi-dimensional SAR tomography

Abstract

In this paper, we extend the previously proposed Tomo-PSInSAR method to detect homogeneous objects in the urban environment. Tomo-PSInSAR integrates conventional persistent scatterer (PS) interferometry and multidimensional SAR tomography to monitor complex built environments [1]. It can jointly detect single and overlaid PSs by constructing a two-tier hierarchical network. Robust estimators (M-estimator and ridge estimator) are introduced to improve the robustness of estimation. To monitor the semi-artificial regions (e.g, pavements and small grassed lands) that are normally distributed scatterers (DSs) in SAR images [2], we analyze homogeneous pixels on the basis of Tomo-PSInSAR. Before estimating the geophysical parameters, we perform a two-sample Anderson-Darling test for the identification of statistically homogeneous pixels at the stage of interferometry. In the first-tier network, the most reliable PSs are identified and they will be used as reference points in the second-tier network. In the second-tier network, the geophysical parameters (e.g., height, deformation velocity) of overlaid PSs are estimated using tomographic imaging [3] and the geophysical parameters of DSs are estimated using the Capon-Beamforming algorithm [4]. The removal of atmospheric delay in the second-tier network is accomplished by subtracting the phase of adjacent PSs that are detected in the first-tier network. In this sense, the proposed integrated method as shown in Fig. 1 can jointly monitor single PSs, overlaid PSs, and DSs according to specific cases. TerraSAR-X/TanDEM-X images are used to validate this method. The results are shown in Fig. 2–4.