Scattering Models in Remote Sensing: Application to SAR Despeckling and Sea Target Detection from GNSS-R Imagery

Imaging sensors are an essential tool for the observation of the Earth’ surface and the study of other celestial bodies. The capability to produce radar images of the illuminated surface is strictly related with the complex phenomenology of the radiation-matter interaction. The electromagnetic scattering theory is a well-established and well-assessed topic in electromagnetics. However, its usage in the remote sensing field is not adequately investigated and studied. This Ph.D. Thesis addresses the exploitation of electromagnetic scattering models suitable for natural surfaces in two applications of remotely sensed data, namely despeckling of synthetic aperture radar (SAR) imagery, and the detection of sea targets in delay-Doppler Maps (DDM) acquired from spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R). The first issue was addressed by conceiving, developing, implementing and validating two despeckling algorithms for SAR images. The developed algorithms introduce some a priori information about the electromagnetic behavior of the resolution cell in the despeckling chain and were conceived as a scattering-based version of pre-existing filters, namely the Probabilistic Patch-Based (PPB) and SAR-Block-Matching 3-D (SARBM3D) algorithms. The scattering behavior of the sensed surface is modeled assuming a fractal surface roughness and using the Small Perturbation Method (SPM) to describe the radar cross section (RCS) of the surface. Performances of the proposed algorithms have been assessed using both canonical test (simulated) and actual images acquired from the COSMO\SkyMed constellation. The robustness of the proposed filters against different error sources, such as the scattering behavior of the surface, surface parameters, Digital Elevation Model (DEM) resolution and the SAR image-DEM coregistration step, has been evaluated via an experimental sensitivity analysis. The problem of detecting sea targets from GNSS-R data in near real-time has been investigated by analyzing the revisit time achieved by constellations of GNSS-R instruments. A statistical analysis of the global revisit time has been performed by means of mission simulation, in which three realistic scenario have been defined. Time requirements for near real-time ship detection purposes are shown to be fulfilled in multi-GNSS constellation scenarios. A four-step sea target has been developed. The detector is a Constant False Alarm Rate (CFAR) algorithm and is based on the suppression of the sea clutter contribution, modeled via the Geometrical Optics (GO) approach. Performance assessment is performed by deriving the Receiver Operating Curves (ROC) of the detector. Finally, the proposed sea target detection algorithm has been tested using actual UK TechDemoSat-1 data.