Individual Scatterer Model Learning for Satellite Interferometry

Abstract

Satellite-based persistent scatterer satellite radar interferometry facilitates the monitoring of deformations of the earth's surface and objects on it. A challenge in data acquisition is the handling of large numbers of coherent radar scatterers. The behavior of each scatterer is time dependent and is influenced by changes in deformation and other phenomena. Built environments are especially challenging since scatterers may have different signal qualities and deformations may vary significantly among objects. Thus, the estimation of the actual deformation requires a functional model and a stochastic model, both of which are typically unknown per scatterer and observation. Here, we present an approach that models the deformation behavior for each individual scatterer. Our technique is applied in a postprocessing phase following the state-of-the-art interferometric processing of persistent scatterers. This addition significantly improves the interpretation of large data sets by separating the relevant phenomena classes more efficiently. It leverages more information than other methods from individual scatterers, which enhances the quality of the estimation and reduces residuals. Our evaluation shows that this technique can discriminate objects in terms of similar deformation characteristics that are independent of the specific spatial position and temporal complexity. Future applications analyzing large data sets collected by satellite radars will, therefore, drastically benefit from this new capability of extracting categorized types of time series behavior. This contribution will augment traditional spatial and temporal analysis and improve the quality of time-dependent deformation assessments.