Advanced Processing Techniques and Applications of Synthetic Aperture Radar Interferometry

Abstract

Synthetic Aperture Radar interferometry (InSAR) is a powerful and established technique, which is based on exploiting the phase difference between pairs of SAR images, and which aims to measure changes in the Earth’s surface. The quality of the interferometric phase is therefore the most crucial factor for deriving reliable products by means of this technique. Unfortunately, the quality of the phase is often degraded due to multiple decorrelation factors, such as the geometrical or temporal decorrelation. Accordingly, central to this PhD thesis is the development of advanced processing techniques and algorithms to extensively reduce such disturbing effects caused by decorrelation. These new techniques include an improved range spectral filter which fully utilizes an external Digital Elevation Model (DEM) to reduce geometrical decorrelation between pairs of SAR images, especially in areas strongly influenced by topography where conventional methods are limited; an improved filter for the final interferometric phase the goal of which is to remove any remaining noise (for instance, noise caused by temporal decorrelation) while, simultaneously, phase details are appropriately preserved; and polarimetric optimization algorithms which also try to enhance the quality of the phase by exploring all the polarization diversity. Moreover, the exploitation of InSAR data for crop type mapping has also been evaluated in this thesis. Specifically, we have tested if the multitemporal interferometric coherence is a valuable feature which can be used as input to a machine learning algorithm to generate thematic maps of crop types. We have shown that InSAR data are sensitive to the temporal evolution of crops, and, hence, they constitute an alternative or a complement to conventional radiometric, SAR-based, classifications.