A Method for Land-Cover Classification of Fully Polarimetric SAR Images by Fusing LiteDSANet and Polarization Feature-Guided DenseCRF

Polarimetric Synthetic Aperture Radar (PolSAR) has significant advantages for land-cover classification for its all-weather, day-and-night, and multi-polarization observation capability. Traditional methods often exhibit limited classification accuracy in regions with strong noise and complex textures. Although deep learning methods can improve classification performance, they usually suffer from high model complexity, while lightweight models often show insufficient spatial consistency. To address these issues, this study proposes a PolSAR land-cover classification framework that integrates a Lightweight Dynamic Sequential Axial Network (LiteDSANet) with a polarization feature-guided Dense Conditional Random Field (PFG-DenseCRF). LiteDSANet is employed to generate the initial class probability map, and PFG-DenseCRF optimizes the classification results by introducing polarimetric features. Experiments were conducted on AIRSAR L-band and RADARSAT-2 C-band datasets from the San Francisco Bay and Flevoland regions, covering agricultural, urban, and natural land-cover scenes. The results show that the proposed method improves classification accuracy by 2.14~15.36% compared with other methods, while achieving a favorable balance between accuracy and computational efficiency. These results demonstrate the effectiveness of the proposed method for PolSAR land-cover classification in different regional environments.