Polarimetric Synthetic Aperture Radar

Polarimetric Synthetic Aperture Radar (PolSAR) uses multiple polarization channels to gather richer information about Earth's surface than conventional SAR, enabling improved land cover classification, change detection, and other applications. Current research heavily emphasizes deep learning techniques, including convolutional neural networks (CNNs), autoencoders, and novel architectures like dual-branch and multi-branch networks, to extract features and improve classification accuracy from PolSAR data, often addressing challenges like speckle noise and limited labeled data. These advancements are improving the accuracy and efficiency of various applications, such as tornado detection, geothermal resource mapping, and precision agriculture, by leveraging the enhanced information content of PolSAR imagery. Furthermore, research is exploring the use of self-supervised learning and Riemannian geometry to better handle the complex nature of PolSAR data.