Integrating linear and circular polarization features for PolSAR land cover classification with deep learning

Existing deep learning methods for ecological monitoring using polarimetric synthetic aperture radar (PolSAR) imagery primarily rely on coherency (T) or covariance (C) matrices derived from the linear polarization basis, often overlooking scattering information inherent in alternative polarization representations. To address this limitation, this study proposes a novel classification framework that explicitly incorporates a circular polarization basis into the PolSAR deep learning workflow. A circular coherency matrix (Cir), analogous to the conventional T matrix, was first derived through polarization basis transformation. Subsequently, a multi-basis input scheme was introduced to fuse linear and circular polarization features to enhance feature representation and information utilization. The proposed framework was validated on two benchmark datasets using multiple deep learning models, achieving state-of-the-art classification accuracies of 97.70% and 98.58%.Compared with standard linear-basis approaches, the proposed scheme yielded accuracy improvements of 2.86% over the T-matrix-based method and 2.26% over the C-matrix-based method. In addition, the incorporation of circular polarization features significantly enhanced physical interpretability, particularly for structurally complex targets such as forests and buildings. Overall, the findings provide an effective technical pathway for intelligent land cover classification and broader ecological monitoring. The source code and datasets are available at https://github.com/zhangssy/Circular-Polarization-Basis-Implementation.