Unified Single/Dual-Frequency PolSAR Land-Cover Segmentation via Single Model With Masked Frequency Training

Dual-frequency (DF) Polarimetric Synthetic Aperture Radar (PolSAR) is crucial for Earth observation, offering richer information than single-frequency (SF) systems for land cover segmentation. However, SF tasks remain prevalent, and current deep learning methods typically require separate models for SF/DF tasks, hindering feature reuse across bands and broader applicability. To overcome this limitation, this letter proposes a unified network with training strategy capable of processing both SF and DF PolSAR images. Leveraging the high spatial similarity yet distinct polarimetric characteristics of DF PolSAR images, the architecture employs a shared backbone consisting of Large Kernel Selective (LKS) for joint feature extraction and Lightweight Band-specific Mixture of Experts (LBME) for capturing distinct patterns across bands. The Decoder employs Mixing Feature Transformer (MFT) blocks to refine fusion features by cross-attention and maintains robustness for SF features by explicit prompts. The proposed unified model achieves higher metrics on both SF (mIoU: 72.58% for L-band, 72.74% for S-band) and DF (mIoU: 75.19% for LS combined) tasks. The code will be made publicly available at URL