Remote Sensing of Urban Areas from Polarimetric SAR Data using Time-Frequency and Spectral Analysis Methods

This article presents two complementary approaches for the study of urban areas using polarimetric and interferometric SAR (POL-inSAR) data. A multidimensional linear Time-Frequency (TF) decomposition is used to analyze the intrinsic polarimetric behavior of different components of an urban area. A TF signal model, adapted to the case of urban areas, is proposed and studied using relevant statistical descriptors. A TF classification procedure is introduced to retrieve building location and characterize their polarimetric response, and applied to fully polarimetric SAR data acquired by the E-SAR sensor at L-band. Multiple POL-inSAR signals, acquired from different positions, are used to estimate the height of buildings using the interferometry principle. High-performance array signal processing techniques are adapted to the case multi-baseline POL-InSAR (MBPI) observations, in order to enhance the height estimation of scatterers by calculating optimal polarization combinations and determining their scattering characteristics. These multi-dimensional spectral estimation methods are shown to resolve the building layover problem by extracting and analyzing two components within one azimuth-range resolution cell.