PolSAR Image

PolSAR (Polarimetric Synthetic Aperture Radar) image analysis focuses on extracting meaningful information from radar data to classify land cover and perform semantic segmentation. Current research emphasizes overcoming challenges like limited labeled data by employing self-supervised learning techniques, including contrastive learning and masked autoencoders, often within heterogeneous network architectures. Advanced models, such as complex-valued neural networks and Riemannian geometry-based convolutional networks, are being developed to directly process the complex nature of PolSAR data, improving classification accuracy and efficiency. These advancements have significant implications for remote sensing applications, enabling more accurate and automated land-use mapping and environmental monitoring.