Καινοτόμες μέθοδοι ανάλυσης δεδομενων τηλεπισκόπησης SAR

Remote Sensing offers vast opportunities to comprehensively study ecosystems by harnessing data from an array of satellite systems. With over 150 Earth-observation satellites currently orbiting the planet, Synthetic Aperture Radar (SAR) emerges as a prominent technology in Earth Observation due to its versatility and wide-ranging applications. Unlike optical imaging, SAR actively transmits electromagnetic waves towards targets and captures their backscattered signals, allowing penetration through clouds, foliage, and surface layers irrespective of day or night conditions. Fully polarimetric SAR, preferred for comprehensive target analysis, elucidates the electromagnetic scatterer's backscattering behavior, providing insights into surface characteristics like geometry, reflectivity, and geophysical properties such as moisture content and roughness. The stage of information processing is critical across various applications leveraging satellite data, motivating ongoing research into diverse methods for extracting meaningful information. This thesis embarks on a comprehensive exploration of satellite data processing, beginning with the theoretical foundations of physics relevant to the field alongside SAR configurations. It delves into Polarimetric Data decomposition techniques for information extraction, culminating in the introduction of the novel Double Scatterer Model, which demonstrates its robustness and significance in classification and detection tasks. After establishing the essential background concerning electromagnetic principles, mathematical tools, and SAR configuration in Chapters 2 and 3, Chapter 4 provides an in-depth analysis of techniques focused on information extraction from fully polarimetric SAR data. These techniques are classified into coherent and non-coherent methods based on their assumptions about the distribution of information among polarimetric cells. The thesis explores both well-established and innovative approaches in polarimetric decomposition within these categories. Pauli decomposition and the Cameron target decomposition are thoroughly analyzed within the coherent category. Transitioning to the non-coherent domain, the thesis investigates the Freeman–Durden decomposition, Yamaguchi’s approach, and the eigenvector–eigenvalue decomposition by Cloude and Pottier. Experimental testing on a benchmark dataset from the Vancouver area validates the efficacy of each method. The introduction of the novel Double Scatterer Model follows, enriching the understanding of polarimetric decomposition techniques and paving the way for enhanced information extraction capabilities. Experimental results confirm the versatility and robustness of the proposed methodology across diverse applications. Through this comprehensive study, the thesis contributes to advancing remote sensing methodologies and their applications in ecosystem analysis and monitoring.

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